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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/sMamooler/PICLe

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PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection

https://github.com/sai990323/tallrec

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TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

https://github.com/sail-sg/lorahub

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LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

https://github.com/salokr/email-event-extraction

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MAILEX: Email Event and Argument Extraction

https://github.com/samuelcahyawijaya/in-context-alignment

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LLMs Are Few-Shot In-Context Low-Resource Language Learners

https://github.com/samuelgabriel/bayesgeneralizations

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Bayes' Power for Explaining In-Context Learning Generalizations

https://github.com/samyateia/bioasq2024

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Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks

https://github.com/sanjayss34/codevqa

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Modular Visual Question Answering via Code Generation

https://github.com/sap-samples/contexttab

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ConTextTab: A Semantics-Aware Tabular In-Context Learner

https://github.com/saturnmars/derivingstructuredinsightsfromsustainabilityreportsvialargelanguagemodels

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Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models

https://github.com/sayantan11995/memesense

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MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation

https://github.com/sciencenlp-lab/llm-ssc

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Multi-label Sequential Sentence Classification via Large Language Model

https://github.com/seharanul17/synthetic-tabular-LLM

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EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

https://github.com/sehyunkwon/task-diversity-icl

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Task Diversity Shortens the ICL Plateau

https://github.com/sejoonoh/atr

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Adversarial Text Rewriting for Text-aware Recommender Systems

https://github.com/selenashe/scone

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ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

https://github.com/shahriargolchin/time-travel-in-llms

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Time Travel in LLMs: Tracing Data Contamination in Large Language Models

https://github.com/shanka123/llm_pfc

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Improving Planning with Large Language Models: A Modular Agentic Architecture

https://github.com/shannonai/gpt-cls-carp

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Text Classification via Large Language Models

https://github.com/shark-nlp/evalm

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In-Context Learning with Many Demonstration Examples

https://github.com/shark-nlp/openicl

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OpenICL: An Open-Source Framework for In-context Learning

https://github.com/shark-nlp/self-adaptive-icl

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Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

https://github.com/shengliu66/icv

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In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering

https://github.com/shiluohe/referencetrustabledecoding

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Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models

https://github.com/shin-ee-chen/bla

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The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models

https://github.com/shivanshu-gupta/gist-icl

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GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks

https://github.com/shivanshu-gupta/icl-coverage

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Coverage-based Example Selection for In-Context Learning

https://github.com/shrahimim/boosting-theory-of-mind-in-llms-with-prompting

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Boosting Theory-of-Mind Performance in Large Language Models via Prompting

https://github.com/shrimonmuke0202/alpapico

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AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs

https://github.com/shuaichenchang/odis-text-to-sql

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Selective Demonstrations for Cross-domain Text-to-SQL

https://github.com/shuaichenchang/prompt-text-to-sql

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How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings

https://github.com/shuzhouyuan/gnnavi

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GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network

https://github.com/sichunluo/recranker

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RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation

https://github.com/silihuang-ai/idt

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In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought

https://github.com/simonucl/adv-retreival-icl

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Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context Learning

https://github.com/simran-arora/focus

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Can Foundation Models Help Us Achieve Perfect Secrecy?

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