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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/leobertolazzi/soft-syllogistic-reasoners
공식 코드A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences
https://github.com/leonardodalinky/zero-shot-gls
공식 코드https://github.com/leooyii/lceg
공식 코드A Controlled Study on Long Context Extension and Generalization in LLMs
https://github.com/lhryang/generalization_of_ft-llm
공식 코드Unveiling the Generalization Power of Fine-Tuned Large Language Models
https://github.com/liampond/llm-rcm
공식 코드https://github.com/lifan-yuan/ood_nlp
공식 코드Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
https://github.com/lijiazheng99/overprompt
공식 코드OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning
https://github.com/lijiefan/laclip
공식 코드https://github.com/lilys012/known_examples
공식 코드Crafting In-context Examples according to LMs' Parametric Knowledge
https://github.com/lingchen0331/uq_icl
공식 코드Uncertainty Quantification for In-Context Learning of Large Language Models
https://github.com/lins-lab/elicit
공식 코드https://github.com/liugangcode/Llamole
공식 코드Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
https://github.com/liujianzhi/echoreel
공식 코드https://github.com/liuyeah/dsare
공식 코드https://github.com/lllindsey0615/composerx
공식 코드https://github.com/llmpfc/icml24sub
공식 코드Improving Planning with Large Language Models: A Modular Agentic Architecture
https://github.com/lmcache/lmcache
공식 코드https://github.com/lockon-n/self-prompting
공식 코드Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
https://github.com/longjubai/eecs598_llm_relationextraction
공식 코드Graphical Reasoning: LLM-based Semi-Open Relation Extraction
https://github.com/ltgoslo/bert-in-context
공식 코드https://github.com/ltl3a87/kb-binder
공식 코드Few-shot In-context Learning for Knowledge Base Question Answering
https://github.com/ltzheng/synapse
공식 코드Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control
https://github.com/luciusssss/zhuangbench
공식 코드Teaching Large Language Models an Unseen Language on the Fly
https://github.com/luckyyysta/fine-grained-attribution
공식 코드Learning Fine-Grained Grounded Citations for Attributed Large Language Models
https://github.com/lugroupumn/icl-ellipticpdes
공식 코드In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
https://github.com/lugroupumn/icl_linear_systems
공식 코드In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
https://github.com/luke-avionics/mg-verilog
공식 코드MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation
https://github.com/luodian/otter
공식 코드Otter: A Multi-Modal Model with In-Context Instruction Tuning
https://github.com/lupantech/promptpg
공식 코드RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
https://github.com/lwbxc/stbench
공식 코드STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
https://github.com/lz1oceani/llm-as-hierarchical-policy
공식 코드https://github.com/lzvv123456/i2cl
공식 코드https://github.com/lzw108/raemollm
공식 코드https://github.com/maarten-deraedt/idas-intent-discovery-with-abstract-summarization
공식 코드https://github.com/maksym-taranukhin/stance_reasoner
공식 코드Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.