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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/kargaranamir/MenuCraft

공식 코드

MenuCraft: Interactive Menu System Design with Large Language Models

https://github.com/karin0018/graphprompter

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GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning

https://github.com/karpurapus/bddgpt-automate-tests

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Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test Formulation

https://github.com/karuna-bhaila/fairicl

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Fair In-Context Learning via Latent Concept Variables

https://github.com/karuna-bhaila/llm_unlearning

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Soft Prompting for Unlearning in Large Language Models

https://github.com/kasmasvan/poe

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POE: Process of Elimination for Multiple Choice Reasoning

https://github.com/kathrinse/math-reasoning-benchmark

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Benchmarking Large Language Models for Math Reasoning Tasks

https://github.com/kayoyin/icl-heads

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Which Attention Heads Matter for In-Context Learning?

https://github.com/kchu/llm-map

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LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language

https://github.com/kclip/cell-free-mimo-icl

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Cell-Free Multi-User MIMO Equalization via In-Context Learning

https://github.com/kclip/icl-equalization

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In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models

https://github.com/kdegroup/mmict

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MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples

https://github.com/keirp/automatic_prompt_engineer

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Large Language Models Are Human-Level Prompt Engineers

https://github.com/kikumaru818/meta_math_scoring

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Automatic Short Math Answer Grading via In-context Meta-learning

https://github.com/kilimanj4r0/code-summarization-beyond-function-level

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Code Summarization Beyond Function Level

https://github.com/kinit-sk/l3d-sensitivity-investigation

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On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices

https://github.com/kiranpurohit/case

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Sample Efficient Demonstration Selection for In-Context Learning

https://github.com/kiranpurohit/explora

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EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning

https://github.com/kohjingyu/fromage

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Grounding Language Models to Images for Multimodal Inputs and Outputs

https://github.com/komeijiforce/cotam

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Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation

https://github.com/kongds/scaling_sentemb

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Scaling Sentence Embeddings with Large Language Models

https://github.com/kothasuhas/understanding-forgetting

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Understanding Catastrophic Forgetting in Language Models via Implicit Inference

https://github.com/krafton-ai/mambaformer-icl

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Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

https://github.com/krasheninnikov/internalization

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Implicit meta-learning may lead language models to trust more reliable sources

https://github.com/kumarmanas/TAMP_COT_TL

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CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning

https://github.com/kvignesh1420/cot-icl-lab

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CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context Demonstrations

https://github.com/lainshower/in-context-calibration

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Rectifying Demonstration Shortcut in In-Context Learning

https://github.com/lancopku/label-words-are-anchors

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Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning

https://github.com/lanqingl/scs

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Visual Prompt Selection for In-Context Learning Segmentation

https://github.com/lapisrocks/languageagenttreesearch

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

https://github.com/launchnlp/ManyICLBench

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Retrieval or Global Context Understanding? On Many-Shot In-Context Learning for Long-Context Evaluation

https://github.com/layer6ai-labs/TabDPT

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TabDPT: Scaling Tabular Foundation Models

https://github.com/lcs2-iiitd/cacn-emnlp-2023

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From Chaos to Clarity: Claim Normalization to Empower Fact-Checking

https://github.com/ldilab/ArchCode

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ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models

https://github.com/leaperovo/adaptivelog-review

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AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model

https://github.com/lechengkong/oneforall

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One for All: Towards Training One Graph Model for All Classification Tasks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.