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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/XHMY/LLM-RankFusion

공식 코드

LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking

https://github.com/Y-Agent/ICL_linear

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In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention

https://github.com/YerevaNN/incontext_spurious

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In-context Learning in Presence of Spurious Correlations

https://github.com/Zhe-Young/SelfCorrectDecompose

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Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs

https://github.com/Zhe-Young/WICL

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Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning

https://github.com/ZhuoZHI-UCL/ICL_multimodal

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Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

https://github.com/a-rebmann/llms4pm

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Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks

https://github.com/a12dongithub/vlmcce

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Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context

https://github.com/aaalgo/postline_0.1

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The Ann Arbor Architecture for Agent-Oriented Programming

https://github.com/aadityasingh/icl-dynamics

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What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

https://github.com/aadityasingh/icl-transience

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The Transient Nature of Emergent In-Context Learning in Transformers

https://github.com/aaronmueller/syntax-icl

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In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax

https://github.com/aatrox103/sap

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Attack Prompt Generation for Red Teaming and Defending Large Language Models

https://github.com/ab-iitd/clitssa

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Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages

https://github.com/abenechehab/dicl

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Zero-shot Model-based Reinforcement Learning using Large Language Models

https://github.com/abhishekpanigrahi1996/transformer_in_transformer

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Trainable Transformer in Transformer

https://github.com/ablghtianyi/ICL_Modular_Arithmetic

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Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks

https://github.com/abx393/llm-pruning-calibration-data

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Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning

https://github.com/activevisionlab/awesome-llm-3d

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When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models

https://github.com/adam-bjtu/w2sg

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Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning

https://github.com/adelwang/kd-cot

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Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

https://github.com/adewynter/metaprompting

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On Meta-Prompting

https://github.com/ag2s1/sibyl-system

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Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning

https://github.com/agi-edgerunners/iil

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All in an Aggregated Image for In-Image Learning

https://github.com/ahmed-abdulhuy/solving-tsp-and-mtsp-combinatorial-challenges-using-visual-reasoning-and-multi-agent-approach-mllms-

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Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges

https://github.com/ai-nikolai/stateact

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StateAct: State Tracking and Reasoning for Acting and Planning with Large Language Models

https://github.com/ai4bharat/ctqscorer

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CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation

https://github.com/ai4fun/dq-lore

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DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

https://github.com/aielawady/relic

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ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI

https://github.com/ailab-cvc/vl-gpt

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VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation

https://github.com/aim-uofa/diffews

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Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation

https://github.com/aim-uofa/sine

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A Simple Image Segmentation Framework via In-Context Examples

https://github.com/alapanju/entsent

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Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies

https://github.com/albertan017/hicl

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HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding

https://github.com/aleflabo/prego

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TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos

https://github.com/aleksandarpetrov/lsrl

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Universal In-Context Approximation By Prompting Fully Recurrent Models

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