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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/boschresearch/eurekaMoments

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Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems

https://github.com/bradleypallen/shroom

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SHROOM-INDElab at SemEval-2024 Task 6: Zero- and Few-Shot LLM-Based Classification for Hallucination Detection

https://github.com/bradyfu/awesome-multimodal-large-language-models

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A Survey on Multimodal Large Language Models

https://github.com/brandon3964/multimodal-task-vector

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Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning

https://github.com/brownfortress/llms-trc

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Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?

https://github.com/budzianowski/multiwoz

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ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?

https://github.com/buswayne/in-context-bldc

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In-Context Learning for Zero-Shot Speed Estimation of BLDC motors

https://github.com/bytedance/e2str

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Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer

https://github.com/c-anwoy/cross-task-icl

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Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks

https://github.com/caap-agent/caap-agent

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CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only

https://github.com/calvinmccarter/tabpfn-eval

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What exactly has TabPFN learned to do?

https://github.com/calvinmccarter/unmasking-trees

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Unmasking Trees for Tabular Data

https://github.com/cambridge-mlg/jolt

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JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs

https://github.com/cambridgeltl/e2e_tod_toolkit

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DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models

https://github.com/cambridgeltl/ensembled-sicl

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On Task Performance and Model Calibration with Supervised and Self-Ensembled In-Context Learning

https://github.com/cambridgeltl/prompt4bli

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On Bilingual Lexicon Induction with Large Language Models

https://github.com/cambridgeltl/sail-bli

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Self-Augmented In-Context Learning for Unsupervised Word Translation

https://github.com/cardinalere/batch-icl

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Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning

https://github.com/casia-iva-lab/anomalygpt

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AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

https://github.com/cfifty/CAML

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Context-Aware Meta-Learning

https://github.com/cfpark00/markov-mixtures

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Competition Dynamics Shape Algorithmic Phases of In-Context Learning

https://github.com/chaitanyamalaviya/pachinko

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What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception

https://github.com/chaitanyamalaviya/rationale_formats

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What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception

https://github.com/chandar-lab/NeoBERT

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NeoBERT: A Next-Generation BERT

https://github.com/changmenseng/generative_calibration

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Generative Calibration for In-context Learning

https://github.com/chanliang/pearl

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PEARL: Towards Permutation-Resilient LLMs

https://github.com/chemfoundationmodels/chemllmbench

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What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks

https://github.com/chen-1031/maple_icl

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MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning

https://github.com/chen-zichen/xplainllm_dataset

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XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs

https://github.com/chen123ctrls/enhancingicl_svdpruning

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Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective

https://github.com/chen700564/metaner-icl

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Learning In-context Learning for Named Entity Recognition

https://github.com/chengsong-huang/lara

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Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning

https://github.com/chenzhongwu20/rulerag_icl_ft

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RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering

https://github.com/chicagohai/active-example-selection

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Active Example Selection for In-Context Learning

https://github.com/choidami/inductive-oocr

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Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data

https://github.com/chongli17/crosslingualalignment

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Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment

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