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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/thu-keg/probtree

Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

https://github.com/vpariza/open-hummingbird-eval

Towards In-context Scene Understanding

https://github.com/wei-ln/versatile-graph-learning-approaches

GraphText: Graph Reasoning in Text Space

https://github.com/wtong98/equality-reasoning

MLPs Learn In-Context on Regression and Classification Tasks

https://github.com/xgenerationlab/xiyan-dbdescgen

A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

https://github.com/xgenerationlab/xiyan_mcp_server

A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

https://github.com/xingchensong/s3tokenizer

FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs

https://github.com/xpitfire/symbolicai

SymbolicAI: A framework for logic-based approaches combining generative models and solvers

https://github.com/xuuhuang/lost_in_the_src

The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation

https://github.com/yangyucheng000/Paper-3/tree/main/FBPrompt

Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering

https://github.com/yangyucheng000/University/tree/main/model-3/seggpt

SegGPT: Segmenting Everything In Context

https://github.com/yingcong-li/dissecting-cot

Transformers as Algorithms: Generalization and Stability in In-context Learning

https://github.com/ylacombe/dataspeech

Natural language guidance of high-fidelity text-to-speech with synthetic annotations

https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text

Do We Still Need Clinical Language Models?

https://github.com/ymoslem/opennmt-tutorial

Fine-tuning Large Language Models for Adaptive Machine Translation

https://github.com/yongchao98/promst

Large Language Models Are Human-Level Prompt Engineers

https://github.com/yuedajiong/looped-transformer/blob/main/looped-transformer.py

Looped Transformers as Programmable Computers

https://github.com/yushuiwx/Mixture-of-LoRA-Experts

LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

https://github.com/zepingyu0512/neuron-attribution

How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning

https://github.com/zihanwangki/x-tc

Prototypical Calibration for Few-shot Learning of Language Models

https://github.com/zt220501/stork

SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

https://github.com/zzxslp/som-llava

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

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