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In-Context Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/huthlab/lm-repeating-text

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Humans and language models diverge when predicting repeating text

https://github.com/hxiaoyang/lm-raven

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In-Context Analogical Reasoning with Pre-Trained Language Models

https://github.com/hzzhou01/hd-loa-prompting

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LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction

https://github.com/hzzhou01/unibias

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UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation

https://github.com/ia-gu/vision-language-in-context-learning-driven-few-shot-visual-inspection-model

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Vision-Language In-Context Learning Driven Few-Shot Visual Inspection Model

https://github.com/iaar-shanghai/grimoire

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Grimoire is All You Need for Enhancing Large Language Models

https://github.com/ibm/ensemble-instruct

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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

https://github.com/ibm/salmon

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SALMON: Self-Alignment with Instructable Reward Models

https://github.com/imagination-research/lbt

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Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study

https://github.com/imperialite/standardize-ctg

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Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation

https://github.com/in-context-learning-2024/in-context

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Can Custom Models Learn In-Context? An Exploration of Hybrid Architecture Performance on In-Context Learning Tasks

https://github.com/inezyu0928/in-context-learning

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Exploring the Robustness of In-Context Learning with Noisy Labels

https://github.com/ink-usc/fewner

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Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER

https://github.com/ink-usc/lifelong-icl

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Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack

https://github.com/ink-usc/request

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Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE

https://github.com/insitro/contextvit

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Contextual Vision Transformers for Robust Representation Learning

https://github.com/instadeepai/metalic

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Metalic: Meta-Learning In-Context with Protein Language Models

https://github.com/intelligent-molecular-systems/llm_finetuning_for_biochemistry

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Leveraging Large Language Models for enzymatic reaction prediction and characterization

https://github.com/internlm/huixiangdou

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HuixiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance

https://github.com/internlm/internlm-math

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InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning

https://github.com/ipa-lab/hackingBuddyGPT

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LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks

https://github.com/ipouyall/can-llms-be-lateral-thinkers

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uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?

https://github.com/iqvianlp/ontologyrag

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OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

https://github.com/isekai-portal/Link-Context-Learning

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Link-Context Learning for Multimodal LLMs

https://github.com/ismailnejjar/im-context

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IM-Context: In-Context Learning for Imbalanced Regression Tasks

https://github.com/itayle/diverse-demonstrations

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Diverse Demonstrations Improve In-context Compositional Generalization

https://github.com/itaymanes/k-qa

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K-QA: A Real-World Medical Q&A Benchmark

https://github.com/ivnle/synth-icl

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Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning Ability

https://github.com/jackieam/e-inmemo

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E-InMeMo: Enhanced Prompting for Visual In-Context Learning

https://github.com/jackieam/inmemo

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Instruct Me More! Random Prompting for Visual In-Context Learning

https://github.com/jacksonwuxs/promptrec

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Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations

https://github.com/jadecurl/hiss

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Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method

https://github.com/jainsameer06/ice

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Multi-Dimensional Evaluation of Text Summarization with In-Context Learning

https://github.com/jamesqian11/subsa

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Sub-SA: Strengthen In-context Learning via Submodular Selective Annotation

https://github.com/jameszhou-gl/gpt-4v-distribution-shift

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How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

https://github.com/jameszhou-gl/icl-distribution-shift

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Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning

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