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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/mikastars39/iclvsfinetune

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Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning

https://github.com/mikelixiang88/context-matters

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Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions

https://github.com/millix19/dbsa

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Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention

https://github.com/minnesotanlp/cobbler

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Benchmarking Cognitive Biases in Large Language Models as Evaluators

https://github.com/miquel-espinosa/samantics

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There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Tasks

https://github.com/mir-mu/coat

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Can In-context Learners Learn a Reasoning Concept from Demonstrations?

https://github.com/mit-han-lab/llm-awq

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VILA: On Pre-training for Visual Language Models

https://github.com/miulab/converser

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CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation

https://github.com/ml-jku/RA-DT

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Retrieval-Augmented Decision Transformer: External Memory for In-context RL

https://github.com/ml-jku/chem-xlstm

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Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

https://github.com/ml-jku/dna-xlstm

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Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

https://github.com/ml-jku/prot-xlstm

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Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

https://github.com/ml-stat-sustech/local-perplexity-ranking

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On the Noise Robustness of In-Context Learning for Text Generation

https://github.com/mlbio-epfl/joint-inference

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Large (Vision) Language Models are Unsupervised In-Context Learners

https://github.com/mlburnham/stance_detection_tutorials

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Stance Detection: A Practical Guide to Classifying Political Beliefs in Text

https://github.com/mmiao2/Hallucination

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Hallucination, Monofacts, and Miscalibration: An Empirical Investigation

https://github.com/mohammadrezapourreza/few-shot-nl2sql-with-prompting

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DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

https://github.com/momarky/impressiongpt

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An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

https://github.com/momarky/radiology-report-extraction

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An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

https://github.com/mominabbass/linc

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Enhancing In-context Learning via Linear Probe Calibration

https://github.com/morning9393/etpo

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Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement

https://github.com/moucheng2017/action-labelling

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In-Context Ensemble Learning from Pseudo Labels Improves Video-Language Models for Low-Level Workflow Understanding

https://github.com/moxmiller/counterfactual-reasoning

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Counterfactual reasoning: an analysis of in-context emergence

https://github.com/mrzhengxin/ctg-regex-instruction

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Toward Unified Controllable Text Generation via Regular Expression Instruction

https://github.com/mrzilinxiao/insgenentitylinking

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Instructed Language Models with Retrievers Are Powerful Entity Linkers

https://github.com/mshukor/EvALign-ICL

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Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning

https://github.com/mshukor/ep-alm

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eP-ALM: Efficient Perceptual Augmentation of Language Models

https://github.com/mtkresearch/generative-fusion-decoding

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Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text Recognition

https://github.com/mukhal/icl-ensembling

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Exploring Demonstration Ensembling for In-context Learning

https://github.com/multimodal-art-projection/yue

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YuE: Scaling Open Foundation Models for Long-Form Music Generation

https://github.com/nafissadeq/reasoning-distillation

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Improving In-Context Learning with Reasoning Distillation

https://github.com/namkoong-lab/PersonalLLM

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PersonalLLM: Tailoring LLMs to Individual Preferences

https://github.com/ncbi/GeneGPT

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GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information

https://github.com/nctu-eva-lab/vhd11k

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T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition

https://github.com/neelabhsinha/qa-prompting

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QA-prompting: Improving Summarization with Large Language Models using Question-Answering

https://github.com/negralessio/thesis-visual-in-context-learning

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Conquering the Retina: Bringing Visual in-Context Learning to OCT

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