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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/openlamm/lamm

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ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models

https://github.com/openmedlab/MedFM

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MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification

https://github.com/opmusic/janus_pii_attack

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The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks

https://github.com/orhonovich/instruction-induction

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Instruction Induction: From Few Examples to Natural Language Task Descriptions

https://github.com/ostars/ruie

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RUIE: Retrieval-based Unified Information Extraction using Large Language Model

https://github.com/osu-nlp-group/llm-planner

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Embodied CoT Distillation From LLM To Off-the-shelf Agents

https://github.com/p-lambda/incontext-learning

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An Explanation of In-context Learning as Implicit Bayesian Inference

https://github.com/paihengxu/xicl

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Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps

https://github.com/paramita08/wikiTransfer

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On the effective transfer of knowledge from English to Hindi Wikipedia

https://github.com/pbhgit/mvcd

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Language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal Reasoning

https://github.com/pbloem/up

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Universal pre-training by iterated random computation

https://github.com/peiyang-song/llm-a-not-b-errors

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In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models

https://github.com/penfever/tabpfn-pt

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TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks

https://github.com/penfever/tunetables

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TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks

https://github.com/persist-lab/syntheticportalgen

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In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages

https://github.com/phenixace/molregpt

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Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective

https://github.com/philotron/bridge-kg

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Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs

https://github.com/physico-benchmark/physico

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The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding

https://github.com/pittnail/icl-minima

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In-Context Learning Functions with Varying Number of Minima

https://github.com/pkhdipraja/in-context-atlas

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The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation

https://github.com/pkseeg/tte_depth

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Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings

https://github.com/pku-ml/longppl

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What is Wrong with Perplexity for Long-context Language Modeling?

https://github.com/pkunlp-icler/ike

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Can We Edit Factual Knowledge by In-Context Learning?

https://github.com/pkunlp-icler/mic

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MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

https://github.com/poseidon-workflows/llm_ad

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Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning

https://github.com/pr0pertygpt/propertygpt

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PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation

https://github.com/pranjalchitale/in-context-mt-analysis

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An Empirical Study of In-context Learning in LLMs for Machine Translation

https://github.com/princeton-nlp/autocompressors

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Adapting Language Models to Compress Contexts

https://github.com/princeton-nlp/cepe

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Long-Context Language Modeling with Parallel Context Encoding

https://github.com/princeton-nlp/edge-pruning

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Finding Transformer Circuits with Edge Pruning

https://github.com/princeton-nlp/mezo

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Fine-Tuning Language Models with Just Forward Passes

https://github.com/princeton-nlp/qurating

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QuRating: Selecting High-Quality Data for Training Language Models

https://github.com/princeton-nlp/transformerprograms

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Learning Transformer Programs

https://github.com/princeton-nlp/whaticllearns

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What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning

https://github.com/progsi/ytuncoverllm

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A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection

https://github.com/psunlpgroup/fish-dip

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Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning

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