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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/vishnutez/in-context-estimation

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Transformers are Provably Optimal In-context Estimators for Wireless Communications

https://github.com/vita-group/dp-opt

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DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

https://github.com/vita-group/superficial_alignment

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Extracting and Understanding the Superficial Knowledge in Alignment

https://github.com/vivek3141/gg-bench

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Measuring General Intelligence with Generated Games

https://github.com/vladhondru25/exddv

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ExDDV: A New Dataset for Explainable Deepfake Detection in Video

https://github.com/vsamuel2003/cie

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CIE: Controlling Language Model Text Generations Using Continuous Signals

https://github.com/waltbai/G2S-TKG-forecasting

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G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models

https://github.com/wang2226/folk

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Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

https://github.com/wangitu/ada-instruct

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Ada-Instruct: Adapting Instruction Generators for Complex Reasoning

https://github.com/wangwenhao0716/anypattern

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AnyPattern: Towards In-context Image Copy Detection

https://github.com/wangxinyilinda/concept-based-demonstration-selection

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Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

https://github.com/wbsg-uni-mannheim/matchgpt

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Using ChatGPT for Entity Matching

https://github.com/wbsg-uni-mannheim/selfrefinement4extractgpt

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Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value Extraction

https://github.com/wbsg-uni-mannheim/tabanngpt

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Evaluating Knowledge Generation and Self-Refinement Strategies for LLM-based Column Type Annotation

https://github.com/wbsg-uni-mannheim/tailormatch

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Fine-tuning Large Language Models for Entity Matching

https://github.com/weiyanbin1999/kicgpt

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KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion

https://github.com/weizeming/mile

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MILE: A Mutation Testing Framework of In-Context Learning Systems

https://github.com/weizhepei/instructrag

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InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales

https://github.com/wenboown/generative-ai-for-semiconductor-physical-design

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Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications

https://github.com/wenhuchen/tablecot

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Large Language Models are few(1)-shot Table Reasoners

https://github.com/wenshansilvia/attention_compressor

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QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression

https://github.com/westenfelder/NL2SH

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LLM-Supported Natural Language to Bash Translation

https://github.com/william-hayes/llms-biased-rl

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Large Language Models are Biased Reinforcement Learners

https://github.com/williamgilpin/dysts

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Zero-shot forecasting of chaotic systems

https://github.com/williamzr/protrix

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ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context

https://github.com/withinmiaov/a-survey-on-mixture-of-experts

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A Survey on Mixture of Experts

https://github.com/wjdghks950/discern-and-answer

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Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise

https://github.com/world1tree/SpeechTranslationRefinement

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Speech Translation Refinement using Large Language Models

https://github.com/wtong98/mlp-icl

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MLPs Learn In-Context on Regression and Classification Tasks

https://github.com/wufeiwuwoshihua/lexicaldiver

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Are LLMs Reliable Translators of Logical Reasoning Across Lexically Diversified Contexts?

https://github.com/wuzengqing001225/consensusdiversitytradeoffmas

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The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems

https://github.com/wxljz/laida

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LaiDA: Linguistics-aware In-context Learning with Data Augmentation for Metaphor Components Identification

https://github.com/x-lance/text2sql-gpt

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ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought

https://github.com/x-lance/text2sql-multiturn-gpt

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CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions

https://github.com/xchang1121/prompt_ICL

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Provable Benefits of Task-Specific Prompts for In-context Learning

https://github.com/xdwang0726/icl_ll

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Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning

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