[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-10733":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":41,"readmeContent":42,"aiSummary":43,"trendingCount":16,"starSnapshotCount":16,"syncStatus":44,"lastSyncTime":45,"discoverSource":46},10733,"prompt-in-context-learning","EgoAlpha\u002Fprompt-in-context-learning","EgoAlpha","Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates. ","https:\u002F\u002Fegoalpha.com",null,"Jupyter Notebook",2241,191,41,4,0,3,12,1,28.85,"MIT License",false,"main",true,[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"ai-agent","chain-of-thought","chatbot","chatgpt","chatgpt-api","cot","in-context-learning","language-modeling","large-language-model","llm","pre-training","prompt","prompt-based-learning","prompt-engineering","prompt-learning","2026-06-12 02:02:25","\u003Cdiv align=\"center\">\n\n\n\u003Cimg src=\".\u002Ffigures\u002FPrompt-EgoAlpha_white.svg\" width=\"600px\">\n\n \u003Cdiv align=\"center\">\n\n [![Typing SVG](https:\u002F\u002Freadme-typing-svg.demolab.com?font=Fira+Code&weight=500&size=30&duration=2500&pause=500&color=8D589A&background=FCFCFF00&center=true&vCenter=true&width=500&lines=Hello!+Human%2C+Are+You+Ready%3F;Welcome+to+my+world!)]()\n \n \u003C\u002Fdiv>\n\n**An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.**\n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n\u003C!-- \u003Ch3 align=\"center\">\n\n    \u003Cp>Resources for prompt learning and engineering; Mastery of LLMs like ChatGPT, GPT3, FlanT5, etc.\u003C\u002Fp>\n\n\u003C\u002Fh3> -->\n\n\u003C!-- \u003Ch4 align=\"center\">\n    \u003Cp>\n        \u003Ca href=\".\u002FREADME.md\">English\u003C\u002Fa> |\n        \u003Ca href=\".\u002Fchatgptprompt_zh.md\">简体中文\u003C\u002Fa>\n    \u003Cp>\n\u003C\u002Fh4> -->\n\n\u003Cp align=\"center\">\n\n  \u003Ca href=\"#📜-papers\">📝 Papers\u003C\u002Fa> |\n  \u003Ca href=\".\u002FPlayground.md\">⚡️  Playground\u003C\u002Fa> |\n  \u003Ca href=\".\u002FPromptEngineering.md\">🛠 Prompt Engineering\u003C\u002Fa> |\n  \u003Ca href=\".\u002Fchatgptprompt.md\">🌍 ChatGPT Prompt\u003C\u002Fa> ｜\n  \u003Ca href=\".\u002Flangchain_guide\u002FLangChainTutorial.ipynb\">⛳ LLMs Usage Guide\u003C\u002Fa> \n\n\u003C\u002Fp>\n\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n\n\u003C!-- ![Build](https:\u002F\u002Fimg.shields.io\u002Fappveyor\u002Fbuild\u002Fgruntjs\u002Fgrunt) -->\n\n![version](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fversion-v3.0.0-green)\n![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)\n\n\u003C!-- ![license](https:\u002F\u002Fimg.shields.io\u002Fbower\u002Fl\u002Fbootstrap?style=plastic) -->\n\n\u003C\u002Fdiv>\n\n> **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let's take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness.\n\nThe resources include:\n\n*🎉[Papers](#📜-papers)🎉*:  The latest papers about *In-Context Learning*, *Prompt Engineering*, *Agent*, and *Foundation Models*. \n\n*🎉[Playground](.\u002FPlayground.md)🎉*:  Large language models（LLMs）that enable prompt experimentation. \n\n*🎉[Prompt Engineering](.\u002FPromptEngineering.md)🎉*: Prompt techniques for leveraging large language models. \n\n*🎉[ChatGPT Prompt](.\u002Fchatgptprompt.md)🎉*: Prompt examples that can be applied in our work and daily lives. \n\n*🎉[LLMs Usage Guide](.\u002Fchatgptprompt.md)🎉*: The method for quickly getting started with large language models by using LangChain.\n\nIn the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): \n- Those who enhance their abilities through the use of AIGC; \n- Those whose jobs are replaced by AI automation.\n\n```\n\n💎EgoAlpha: Hello! human👤, are you ready?\n\n```  \n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n# Table of Contents\n- [🔥 AI Spotlight](#-ai-spotlight-trending-research-papers)\n- [📜 Papers](#-papers)\n  - [Survey](#survey)\n  - [Prompt Engineering](#prompt-engineering)\n    - [Prompt Design](#prompt-design)\n    - [Chain of Thought](#chain-of-thought)\n    - [In-context Learning](#in-context-learning)\n    - [Retrieval Augmented Generation](#retrieval-augmented-generation)\n    - [Evaluation \\& Reliability](#evaluation--reliability)\n  - [Agent](#agent)\n  - [Multimodal Prompt](#multimodal-prompt)\n  - [Prompt Application](#prompt-application)\n  - [Foundation Models](#foundation-models)\n- [👨‍💻 LLM Usage](#-llm-usage)\n- [✉️ Contact](#️-contact)\n- [🙏 Acknowledgements](#-acknowledgements)\n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n# 🔥 AI Spotlight: Trending Research Papers\n\u003C!-- 🔥🔥🔥 -->\n\u003C!-- ☄️ **May 1, 2025** *– Buzzing papers everyone’s talking about* -->\n\n\n\n### **[2026-05-02]**\n\n[**Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems**](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2604.14228) （**New**）\n\n*Published: 2026-04-14*\n\n\u003Cfont color=\"gray\">Jiacheng Liu, Xiaohan Zhao, Xinyi Shang, Zhiqiang Shen - [arXiv]\u003C\u002Ffont>\n\n[![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub%20Stars-929-blue)](https:\u002F\u002Fgithub.com\u002FVILA-Lab\u002FDive-into-Claude-Code) ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-10-red) ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-2-9cf)\n\n---\n\n\n[**Geometric Context Transformer for Streaming 3D Reconstruction**](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2604.14141) （**New**）\n\n*Published: 2026-04-16*\n\n\u003Cfont color=\"gray\">Lin-Zhuo Chen, Jian Gao, Yihang Chen, Ka Leong Cheng, Yipengjing Sun, Liangxiao Hu, Nan Xue, Xing Zhu, Yujun Shen, Yao Yao, Yinghao Xu - [arXiv]\u003C\u002Ffont>\n\n[![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub%20Stars-5,496-blue)](https:\u002F\u002Fgithub.com\u002Frobbyant\u002Flingbot-map) ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-11-red)\n\n---\n\n\n[**LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model**](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2604.20796) （**New**）\n\n*Published: 2026-04-22*\n\n\u003Cfont color=\"gray\">Inclusion AI, Tiwei Bie, Haoxing Chen, Tieyuan Chen, Zhenglin Cheng, Long Cui, Kai Gan, Zhicheng Huang, Zhenzhong Lan, Haoquan Li, Jianguo Li, Tao Lin, Qi Qin, Hongjun Wang, Xiaomei Wang, Haoyuan Wu,  - [arXiv]\u003C\u002Ffont>\n\n[![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub%20Stars-719-blue)](https:\u002F\u002Fgithub.com\u002FinclusionAI\u002FLLaDA2.0-Uni) ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-2-red)\n\n---\n\n\n[**GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)**](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2604.17091) （**New**）\n\n*Published: 2026-04-18*\n\n\u003Cfont color=\"gray\">Jiaqing Liang, Jinyi Han, Weijia Li, Xinyi Wang, Zhoujia Zhang, Zishang Jiang, Ying Liao, Tingyun Li, Ying Huang, Hao Shen, Hanyu Wu, Fang Guo, Keyi Wang, Zhonghua Hong, Zhiyu Lu, Lipeng Ma, Sihang Ji - [arXiv]\u003C\u002Ffont>\n\n[![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub%20Stars-8,623-blue)](https:\u002F\u002Fgithub.com\u002Flsdefine\u002FGenericAgent)\n\n---\n\n\n[**OpenGame: Open Agentic Coding for Games**](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2604.18394) （**New**）\n\n*Published: 2026-04-20*\n\n\u003Cfont color=\"gray\">Yilei Jiang, Jinyuan Hu, Qianyin Xiao, Yaozhi Zheng, Ruize Ma, Kaituo Feng, Jiaming Han, Tianshuo Peng, Kaixuan Fan, Manyuan Zhang, Xiangyu Yue - [arXiv]\u003C\u002Ffont>\n\n[![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub%20Stars-1,775-blue)](https:\u002F\u002Fgithub.com\u002Fleigest519\u002FOpenGame)\n\n---\n\n\n\n\n[👉 Complete history news 👈](.\u002Fhistorynews.md)\n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n---\n\n# 📜 Papers\n\n> You can directly click on the title to jump to the corresponding PDF link location\n\n## Survey\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n[**Motion meets Attention: Video Motion Prompts**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.03179) （**2024.07.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Towards a Personal Health Large Language Model**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.06474) （**2024.06.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-2-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-6-red)\n\n[**Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.06469) （**2024.06.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-14-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-228-blue)](https:\u002F\u002Fgithub.com\u002Fagent-husky\u002Fhusky-v1)\n\n[**Towards Lifelong Learning of Large Language Models: A Survey**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.06391) （**2024.06.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**Towards Semantic Equivalence of Tokenization in Multimodal LLM**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.05127) （**2024.06.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-4-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-6-red)\n\n[**LLMs Meet Multimodal Generation and Editing: A Survey**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.19334) （**2024.05.29**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-2-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-206-blue)](https:\u002F\u002Fgithub.com\u002Fyingqinghe\u002Fawesome-llms-meet-multimodal-generation)\n\n[**Tool Learning with Large Language Models: A Survey**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.17935) （**2024.05.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-3-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-106-blue)](https:\u002F\u002Fgithub.com\u002Fquchangle1\u002Fllm-tool-survey)\n\n[**When LLMs step into the 3D World: A Meta-Analysis of 3D Tasks via Multi-modal Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.10255) （**2024.05.16**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-833-blue)](https:\u002F\u002Fgithub.com\u002Factivevisionlab\u002Fawesome-llm-3d)\n\n[**Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2404.15993) （**2024.04.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**A Survey on the Memory Mechanism of Large Language Model based Agents**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2404.13501) （**2024.04.21**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-4-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-79-blue)](https:\u002F\u002Fgithub.com\u002Fnuster1128\u002Fllm_agent_memory_survey)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Survey\"](.\u002FPaperList\u002Fsurvey.md)👈\n\n## Prompt Engineering\n\n### Prompt Design\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**LLaRA: Supercharging Robot Learning Data for Vision-Language Policy**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.20095) （**2024.06.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-3-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-77-blue)](https:\u002F\u002Fgithub.com\u002Flostxine\u002Fllara)\n\n[**Dataset Size Recovery from LoRA Weights**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19395) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Dual-Phase Accelerated Prompt Optimization**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.13443) （**2024.06.19**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12824) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-8-red)\n\n[**VoCo-LLaMA: Towards Vision Compression with Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12275) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-10-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-58-blue)](https:\u002F\u002Fgithub.com\u002FYxxxb\u002FVoCo-LLaMA)\n\n[**LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12832) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**The Impact of Initialization on LoRA Finetuning Dynamics**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.08447) （**2024.06.12**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-3-red)\n\n[**An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.05130) （**2024.06.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)\n\n[**Cross-Context Backdoor Attacks against Graph Prompt Learning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.17984) （**2024.05.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**Yuan 2.0-M32: Mixture of Experts with Attention Router**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.17976) （**2024.05.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-160-blue)](https:\u002F\u002Fgithub.com\u002Fieit-yuan\u002Fyuan2.0-m32)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Prompt Design\"](.\u002FPaperList\u002FPromptDesignList.md)👈\n\n### Chain of Thought\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.05130) （**2024.06.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)\n\n[**Cantor: Inspiring Multimodal Chain-of-Thought of MLLM**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2404.16033) （**2024.04.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.03361) （**2024.04.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-5-blue)](https:\u002F\u002Fgithub.com\u002Fnicolay-r\u002Fthor-ecac)\n\n[**Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.03622) （**2024.04.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-25-red)\n\n[**Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.03414) （**2024.04.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-17-red)\n\n[**Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.16999) （**2024.03.25**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-16-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-63-blue)](https:\u002F\u002Fgithub.com\u002Fdeepcs233\u002Fvisual-cot)\n\n[**A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.14565) （**2024.03.21**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07376) （**2024.03.12**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-5-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-18-blue)](https:\u002F\u002Fgithub.com\u002Fexpectorlin\u002Fnavcot)\n\n[**ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.06932) （**2024.03.11**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-6-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-27-blue)](https:\u002F\u002Fgithub.com\u002Foceanntwt\u002Fera-cot)\n\n[**Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.05518) （**2024.03.08**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Chain of Thought\"](.\u002FPaperList\u002FChainofThoughtList.md)👈\n\n### In-context Learning\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12832) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**The Impact of Initialization on LoRA Finetuning Dynamics**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.08447) （**2024.06.12**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-3-red)\n\n[**An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.05130) （**2024.06.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)\n\n[**Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.02547) （**2024.06.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-6-blue)](https:\u002F\u002Fgithub.com\u002Fshowlab\u002FVisInContext)\n\n[**Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.02550) （**2024.06.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-2-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-3-blue)](https:\u002F\u002Fgithub.com\u002Fablghtianyi\u002FICL_Modular_Arithmetic)\n\n[**Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.17915) （**2024.05.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-39-blue)](https:\u002F\u002Fgithub.com\u002FOctober2001\u002FProLong)\n\n[**Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.11464) （**2024.05.19**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.11446) （**2024.05.19**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2404.16807) （**2024.04.25**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**Stronger Random Baselines for In-Context Learning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2404.13020) （**2024.04.19**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-1-blue)](https:\u002F\u002Fgithub.com\u002Fgyauney\u002Fmax-random-baseline)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"In-context Learning\"](.\u002FPaperList\u002FInContextLearningList.md)👈\n\n### Retrieval Augmented Generation\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.16989) （**2024.06.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Enhancing RAG Systems: A Survey of Optimization Strategies for Performance and Scalability**](https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijsrem35402) （**2024.06.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.20978) （**2024.05.31**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.16178) （**2024.05.25**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**DocReLM: Mastering Document Retrieval with Language Model**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.11461) （**2024.05.19**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**UniRAG: Universal Retrieval Augmentation for Multi-Modal Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.10311) （**2024.05.16**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**ChatHuman: Language-driven 3D Human Understanding with Retrieval-Augmented Tool Reasoning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.04533) （**2024.05.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.02228) （**2024.05.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)\n\n[**Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.06910) （**2024.04.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-14-red)\n\n[**Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.03577) （**2024.04.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-1-blue)](https:\u002F\u002Fgithub.com\u002Fthu-keg\u002Fknot)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Retrieval Augmented Generation\"](.\u002FPaperList\u002FKnowledgeAugmentedPromptList.md)👈\n\n\n### Evaluation & Reliability\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**CELLO: Causal Evaluation of Large Vision-Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19131) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-4-blue)](https:\u002F\u002Fgithub.com\u002Fopencausalab\u002Fcello)\n\n[**PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.18528) （**2024.06.26**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.16866) （**2024.06.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-5-blue)](https:\u002F\u002Fgithub.com\u002Fjierunchen\u002Fref-l4)\n\n[**OR-Bench: An Over-Refusal Benchmark for Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.20947) （**2024.05.31**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.18027) （**2024.05.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.14555) （**2024.05.23**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.10299) （**2024.05.16**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-9-blue)](https:\u002F\u002Fgithub.com\u002Fautoml\u002Fhw-gpt-bench)\n\n[**Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.06634) （**2024.05.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-1-blue)](https:\u002F\u002Fgithub.com\u002Fevanup\u002Fvna_benchmark)\n\n[**Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.02287) （**2024.05.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-6-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-139-blue)](https:\u002F\u002Fgithub.com\u002Freka-ai\u002Freka-vibe-eval)\n\n[**Causal Evaluation of Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.00622) （**2024.05.01**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-42-blue)](https:\u002F\u002Fgithub.com\u002FOpenCausaLab\u002FCaLM)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Evaluation & Reliability\"](.\u002FPaperList\u002FEvaluationReliabilityList.md)👈\n\n## Agent\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**Cooperative Multi-Agent Deep Reinforcement Learning Methods for UAV-aided Mobile Edge Computing Networks**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.03280) （**2024.07.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Symbolic Learning Enables Self-Evolving Agents**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.18532) （**2024.06.26**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-8-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-4.8k-blue)](https:\u002F\u002Fgithub.com\u002Faiwaves-cn\u002Fagents)\n\n[**Adversarial Attacks on Multimodal Agents**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12814) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-24-blue)](https:\u002F\u002Fgithub.com\u002Fchenwu98\u002Fagent-attack)\n\n[**DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11896) （**2024.06.14**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-5-red)\n\n[**Transforming Wearable Data into Health Insights using Large Language Model Agents**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.06464) （**2024.06.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-2-red)\n\n[**Neuromorphic dreaming: A pathway to efficient learning in artificial agents**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.15616) （**2024.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.10292) （**2024.05.16**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-3-green)\n\n[**Learning Multi-Agent Communication from Graph Modeling Perspective**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.08550) （**2024.05.14**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-3-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-7-blue)](https:\u002F\u002Fgithub.com\u002Fcharleshsc\u002FCommFormer)\n\n[**Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.05955) （**2024.05.09**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-10-blue)](https:\u002F\u002Fgithub.com\u002Ffreedomintelligence\u002Fsmurfs)\n\n[**Unveiling Disparities in Web Task Handling Between Human and Web Agent**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.04497) （**2024.05.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Agent\"](.\u002FPaperList\u002FAgentList.md)👈\n\n## Multimodal Prompt\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.03320) （**2024.07.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-4-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-2.0k-blue)](https:\u002F\u002Fgithub.com\u002Finternlm\u002Finternlm-xcomposer)\n\n[**LLaRA: Supercharging Robot Learning Data for Vision-Language Policy**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.20095) （**2024.06.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-3-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-77-blue)](https:\u002F\u002Fgithub.com\u002Flostxine\u002Fllara)\n\n[**Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.20098) （**2024.06.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-33-blue)](https:\u002F\u002Fgithub.com\u002Fmbzuai-llm\u002Fweb2code)\n\n[**LLaVolta: Efficient Multi-modal Models via Stage-wise Visual Context Compression**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.20092) （**2024.06.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-20-blue)](https:\u002F\u002Fgithub.com\u002Fbeckschen\u002Fllavolta)\n\n[**Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.16860) （**2024.06.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-67-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-1.4k-blue)](https:\u002F\u002Fgithub.com\u002Fcambrian-mllm\u002Fcambrian)\n\n[**VoCo-LLaMA: Towards Vision Compression with Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12275) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-10-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-58-blue)](https:\u002F\u002Fgithub.com\u002FYxxxb\u002FVoCo-LLaMA)\n\n[**Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.08487) （**2024.06.12**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-11-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-90-blue)](https:\u002F\u002Fgithub.com\u002Fyfzhang114\u002Fslime)\n\n[**An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.05130) （**2024.06.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)\n\n[**Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.02547) （**2024.06.04**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-6-blue)](https:\u002F\u002Fgithub.com\u002Fshowlab\u002FVisInContext)\n\n[**DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.20985) （**2024.05.31**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Multimodal Prompt\"](.\u002FPaperList\u002Fmultimodalprompt.md)👈\n\n## Prompt Application\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.02956) （**2024.07.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.20098) （**2024.06.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-33-blue)](https:\u002F\u002Fgithub.com\u002Fmbzuai-llm\u002Fweb2code)\n\n[**OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19389) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-2-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-7-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-934-blue)](https:\u002F\u002Fgithub.com\u002Flxtgh\u002Fomg-seg)\n\n[**Adversarial Search Engine Optimization for Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.18382) （**2024.06.26**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**VideoLLM-online: Online Video Large Language Model for Streaming Video**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11816) （**2024.06.17**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Regularizing Hidden States Enables Learning Generalized Reward Model for LLMs**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.10216) （**2024.06.14**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.06525) （**2024.06.10**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-1-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-43-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-957-blue)](https:\u002F\u002Fgithub.com\u002Ffoundationvision\u002Fllamagen)\n\n[**Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.04988) （**2024.06.07**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**PaCE: Parsimonious Concept Engineering for Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.04331) （**2024.06.06**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-15-blue)](https:\u002F\u002Fgithub.com\u002Fpeterljq\u002Fparsimonious-concept-engineering)\n\n[**Yuan 2.0-M32: Mixture of Experts with Attention Router**](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2405.17976) （**2024.05.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-160-blue)](https:\u002F\u002Fgithub.com\u002Fieit-yuan\u002Fyuan2.0-m32)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Prompt Application\"](.\u002FPaperList\u002Fpromptapplication.md)👈\n\n## Foundation Models\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.03203) （**2024.07.03**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning**](https:\u002F\u002Fdoi.org\u002F10.1109\u002FTCSVT.2024.3358850) （**2024.07.01**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-3-green)\n\n[**Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.20086) （**2024.06.28**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)\n\n[**OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19389) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-2-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-7-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-934-blue)](https:\u002F\u002Fgithub.com\u002Flxtgh\u002Fomg-seg)\n\n[**Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19354) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-2-red)\n\n[**Efficient World Models with Context-Aware Tokenization**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19320) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-49-blue)](https:\u002F\u002Fgithub.com\u002Fvmicheli\u002Fdelta-iris)\n\n[**The Remarkable Robustness of LLMs: Stages of Inference?**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.19384) （**2024.06.27**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-7-red)\n\n[**ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.18125) （**2024.06.26**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-1-blue)](https:\u002F\u002Fgithub.com\u002Fnoran-mohamed\u002FResume-Classification-Dataset)\n\n[**AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.12805) （**2024.06.18**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-4-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-5-blue)](https:\u002F\u002Fgithub.com\u002Fitsmag11\u002Faitti)\n\n[**Unveiling Encoder-Free Vision-Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11832) （**2024.06.17**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-6-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-75-blue)](https:\u002F\u002Fgithub.com\u002Fbaaivision\u002Feve)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Foundation Models\"](.\u002FPaperList\u002Ffoundationmodels.md)👈\n\n\u003C!-- ### 📌 Hard Prompt\u002F Discrete Prompt\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n[**Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15265) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-4-blue)](https:\u002F\u002Fgithub.com\u002Fzirui-ray-liu\u002Fwtacrs)\n\n[**How to Distill your BERT: An Empirical Study on the Impact of Weight Initialisation and Distillation Objectives**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15032) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-14-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-9-blue)](https:\u002F\u002Fgithub.com\u002Fmainlp\u002Fhow-to-distill-your-bert)\n\n[**ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15024) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-93-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-38-blue)](https:\u002F\u002Fgithub.com\u002Falbert-jin\u002Fagricultural_textual_classification_chatgpt)\n\n[**Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15023) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-63-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-491-blue)](https:\u002F\u002Fgithub.com\u002Fluogen1996\u002Flavin)\n\n[**LLMDet: A Large Language Models Detection Tool**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15004) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-11-red)\n\n[**OverPrompt: Enhancing ChatGPT Capabilities through an Efficient In-Context Learning Approach**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14973) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-1-red)\n\n[**Interpretable by Design Visual Question Answering**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14882) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-4-red)\n\n[**In-Context Demonstration Selection with Cross Entropy Difference**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14726) （**2023.05.24**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-10-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-3.4k-blue)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Flmops)\n\n[**LogicLLM: Exploring Self-supervised Logic-enhanced Training for Large Language Models**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13718) （**2023.05.23**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-14-red)\n\n[**Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer**](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12761) （**2023.05.22**）\n\n![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCitations-0-green)  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMendeley%20Readers-12-red)  [![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub%20Stars-2-blue)](https:\u002F\u002Fgithub.com\u002Fthu-bpm\u002Fsoftmv)\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Hard Prompt\"](.\u002FPaperList\u002FHardPromptList.md)👈\n\n### 📌 Soft Prompt\u002F Continuous Prompt\n\n\u003Cdiv style=\"line-height:0.2em;\">\n\n\n\n\n\u003C\u002Fdiv>\n\n👉[Complete paper list 🔗 for \"Soft Prompt\"](.\u002FPaperList\u002FSoftPromptList.md)👈 -->\n\n\u003C!-- ## Prompt for Knowledge Graph\n\n\u002F\u002F __PAPER_LIST__:{field:'Prompt Design',size:10,state:'corrected',type:'lite'}\n\n👉[Complete paper list 🔗 for \"Prompt for Knowledge Graph\"](.\u002FPaperList\u002FPromptKnowledgeGraphList.md)👈 --> \n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n\u003C!-- # 🎓 Citation\n\nIf you find our work helps, please star our project and cite our paper. Thanks a lot!\n\n```\n\n综述论文可以放在这个位置\n\n``` -->\n\n\u003C!-- \u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F> -->\n\n# 👨‍💻 LLM Usage\nLarge language models (LLMs) are becoming a revolutionary technology that is shaping the development of our era. Developers can create applications that were previously only possible in our imaginations by building LLMs. However, using these LLMs often comes with certain technical barriers, and even at the introductory stage, people may be intimidated by cutting-edge technology: Do you have any questions like the following?\n\n- ❓ *How can LLM be built using programming?* \n- ❓ *How can it be used and deployed in your own programs?* \n\n💡 If there was a tutorial that could be accessible to all audiences, not just computer science professionals, it would provide detailed and comprehensive guidance to quickly get started and operate in a short amount of time, ultimately achieving the goal of being able to use LLMs flexibly and creatively to build the programs they envision. And now, just for you: the most detailed and comprehensive Langchain beginner's guide, sourced from the official langchain website but with further adjustments to the content, accompanied by the most detailed and annotated code examples, teaching code lines by line and sentence by sentence to all audiences.\n\n**Click 👉[here](.\u002Flangchain_guide\u002FLangChainTutorial.ipynb)👈 to take a quick tour of getting started with LLM.**\n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n# ✉️ Contact\n\nThis repo is maintained by [EgoAlpha Lab](https:\u002F\u002Fgithub.com\u002FEgoAlpha). Questions and discussions are welcome via `helloegoalpha@gmail.com`.\n\nWe are willing to engage in discussions with friends from the academic and industrial communities, and explore the latest developments in prompt engineering and in-context learning together.\n\n\u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F>\n\n# 🙏 Acknowledgements\n\nThanks to the PhD students from [EgoAlpha Lab](https:\u002F\u002Fgithub.com\u002FEgoAlpha) and other workers who participated in this repo. We will improve the project in the follow-up period and maintain this community well. We also would like to express our sincere gratitude to the authors of the relevant resources. Your efforts have broadened our horizons and enabled us to perceive a more wonderful world.\n\n\n\u003C!-- \u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F> -->\n\n\u003C!-- # 👨‍👩‍👧‍👦 Contributors\n\n## Main Contributors\n* [Yu Liu]()\n* [Yifei Cao](https:\u002F\u002Fgithub.com\u002Fcyfedu1024)\n* [Jizhe Yu]()\n* [Yuan Yao]()\n* [He Qi]() -->\n\n\n\u003C!-- ## Guest Contributors\n* [No] -->\n\n\u003C!-- \u003Cimg width=\"200%\" src=\".\u002Ffigures\u002Fhr.gif\" \u002F> -->\n\n\u003C!-- # 📔 License\n\nThis project is open source and available under the MIT\n\n\u003Cdiv align=\"center\">\n\u003Cimg src=\".\u002Ffigures\u002Frocket.png\"\u002F>\n\u003C\u002Fdiv> -->","该项目是EgoAlpha实验室开源的一个针对上下文学习和提示工程的指南。它提供了关于如何掌握像ChatGPT、GPT-3以及FlanT5等大型语言模型的最新资源和技术更新。核心功能包括最新的研究论文分享、一个用于实验大型语言模型的游乐场环境、提示工程技术详解以及适用于日常工作生活的ChatGPT提示示例。此外，还有一份快速上手大型语言模型使用的指导手册。此项目适合希望深入了解并利用最新AI技术的研究人员、开发者及任何对提升自身技能感兴趣的个人使用。",2,"2026-06-11 03:29:55","top_topic"]