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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/aleksandarpetrov/prefix-tuning-theory

공식 코드

When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations

https://github.com/alexcapstick/llm-elicited-priors

공식 코드

AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

https://github.com/ali-bahrainian/rag_best_practices

공식 코드

Enhancing Retrieval-Augmented Generation: A Study of Best Practices

https://github.com/alibaba-nlp/vec-ra-odqa

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Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts

https://github.com/alibaba/route

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ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL

https://github.com/alienkevin/frogger

공식 코드

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents

https://github.com/alipay/mobile-agent

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MobileAgent: enhancing mobile control via human-machine interaction and SOP integration

https://github.com/aliwister/ast-icl

공식 코드

Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text

https://github.com/allenai/catwalk

공식 코드

Catwalk: A Unified Language Model Evaluation Framework for Many Datasets

https://github.com/allenai/code-semparse

공식 코드

Leveraging Code to Improve In-context Learning for Semantic Parsing

https://github.com/allenai/easy-to-hard-generalization

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The Unreasonable Effectiveness of Easy Training Data for Hard Tasks

https://github.com/allenai/revisiting_surface_form_competition

공식 코드

Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy

https://github.com/alphadl/languageaware_tuning

공식 코드

Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning

https://github.com/alrope123/rethinking-demonstrations

공식 코드

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

https://github.com/alrope123/z-icl

공식 코드

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

https://github.com/alsace08/meta-reasoning

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Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models

https://github.com/amazon-science/adaptive-in-context-learning

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Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection

https://github.com/amazon-science/learning-to-generate-answers-with-citations

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Learning to Generate Answers with Citations via Factual Consistency Models

https://github.com/amazon-science/llm-interpret

공식 코드

Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale

https://github.com/amurtadha/nbce-master

공식 코드

Naive Bayes-based Context Extension for Large Language Models

https://github.com/ancientshi/erm4

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Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems

https://github.com/andychiangsh/badge

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BADGE: BADminton report Generation and Evaluation with LLM

https://github.com/andychiangsh/pre-cofactv3

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Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning

https://github.com/andyz245/LanguageAgentTreeSearch

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Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

https://github.com/anirudhsom/capp-dataset

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Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning

https://github.com/anomalyllm/anomalyllm

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AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

https://github.com/anonymouscode-computerscience/ride

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RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars

https://github.com/anum94/adapteval

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AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization

https://github.com/apple/ml-rpm-bench

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How Far Are We from Intelligent Visual Deductive Reasoning?

https://github.com/applied-machine-learning-lab/notellm

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NoteLLM-2: Multimodal Large Representation Models for Recommendation

https://github.com/aridhasan/mubase

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Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis

https://github.com/arikreuter/icl_for_full_bayesian_inference

공식 코드

Can Transformers Learn Full Bayesian Inference in Context?

https://github.com/armelrandy/icl-mt

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In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation

https://github.com/arthur-heng/nlgraph

공식 코드

Can Language Models Solve Graph Problems in Natural Language?

https://github.com/arthurizijar/kb-coder

공식 코드

Code-Style In-Context Learning for Knowledge-Based Question Answering

https://github.com/arumaekawa/dilm

공식 코드

DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation

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