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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/siqilii/retrieve-and-demonstration-st

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Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach

https://github.com/sislab-unitn/fine-tune-or-rag

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Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue

https://github.com/siyan-zhao/ICL_decision_boundary

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Probing the Decision Boundaries of In-context Learning in Large Language Models

https://github.com/sjdai/llm-thematic-analysis

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LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis

https://github.com/sky-goldfish/sail

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SAIL: Sample-Centric In-Context Learning for Document Information Extraction

https://github.com/sled-group/heuristic-analytic-reasoning

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From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning

https://github.com/smartmediaai/personalsum

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PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models

https://github.com/social-ai-studio/mod_hate

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Modularized Networks for Few-shot Hateful Meme Detection

https://github.com/sohanpatnaik106/cabinet_qa

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CABINET: Content Relevance based Noise Reduction for Table Question Answering

https://github.com/songlei00/ribbo

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Reinforced In-Context Black-Box Optimization

https://github.com/songtaoliu0823/fusionretro

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FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning

https://github.com/songxiaoshuai/OOD-Evaluation

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Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT

https://github.com/sonlam1102/zefav

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ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification

https://github.com/sparkjiao/logicllm

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Exploring Self-supervised Logic-enhanced Training for Large Language Models

https://github.com/sparkjiao/merit-v2

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Exploring Self-supervised Logic-enhanced Training for Large Language Models

https://github.com/spirit-moon-fly/CAL

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Causal-Guided Active Learning for Debiasing Large Language Models

https://github.com/srsawant34/efficient_instruction_learning

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Instruction Tuned Models are Quick Learners

https://github.com/stable-lab/pro-v

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PRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification

https://github.com/stan-lei/kwaichat-naacl2025

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KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus

https://github.com/stanford-oval/spinach

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SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions

https://github.com/stanford-oval/suql

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SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models

https://github.com/stanfordmimi/clin-bhc-summ

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A Dataset and Benchmark for Hospital Course Summarization with Adapted Large Language Models

https://github.com/stanfordmlgroup/ManyICL

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Many-Shot In-Context Learning in Multimodal Foundation Models

https://github.com/stanfordnlp/dsp

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Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

https://github.com/stevekgyang/mentalllama

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Towards Interpretable Mental Health Analysis with Large Language Models

https://github.com/stevekgyang/metaaligner

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MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models

https://github.com/stevenzhb/cot_causal_analysis

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How Likely Do LLMs with CoT Mimic Human Reasoning?

https://github.com/stijnverdenius/lat-pfn

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LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting

https://github.com/stjohn2007/likelihood_bias

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Likelihood-based Mitigation of Evaluation Bias in Large Language Models

https://github.com/sunanhe/meddr

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GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration

https://github.com/sungwon-han/featllm

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Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning

https://github.com/sunzey/x-prompt

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X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models

https://github.com/superbooming/e-redial

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Towards Explainable Conversational Recommender Systems

https://github.com/suprityoung/faima

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FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis

https://github.com/surrey-nlp/LLMs4MTQE-UGC

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Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?

https://github.com/sustcsonglin/flash-linear-attention

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Linear Transformers with Learnable Kernel Functions are Better In-Context Models

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