https://github.com/flairox/cultural-accumulation
공식 코드Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning
Papers with Code 아카이브
In-Context Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/flyingfeather/dea-sql
공식 코드Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm
https://github.com/fmh1art/batcher
공식 코드Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
https://github.com/fminference/dejavu
공식 코드Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
https://github.com/forgi86/sysid-neural-transformers
공식 코드From system models to class models: An in-context learning paradigm
https://github.com/forgi86/sysid-transformers
공식 코드From system models to class models: An in-context learning paradigm
https://github.com/forjadeforest/icd-lm
공식 코드Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
https://github.com/forjadeforest/lever-lm
공식 코드Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
https://github.com/forjadeforest/live-learnable-in-context-vector
공식 코드LIVE: Learnable In-Context Vector for Visual Question Answering
https://github.com/franxyao/gpt-bargaining
공식 코드Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
https://github.com/fshp971/adv-icl
공식 코드https://github.com/fumiyauchiyama/code_pretraining
공식 코드https://github.com/funaudiollm/cosyvoice
공식 코드https://github.com/furrutiav/nllf-emnlp-2023
공식 코드Deep Natural Language Feature Learning for Interpretable Prediction
https://github.com/fuyu-quant/iblm
공식 코드Inductive-bias Learning: Generating Code Models with Large Language Model
https://github.com/fywalter/label-bias
공식 코드https://github.com/garyjiajia/ofv2_icl_vqa
공식 코드How to Configure Good In-Context Sequence for Visual Question Answering
https://github.com/gblackout/lm-os
공식 코드https://github.com/gchochla/aggregation-artifacts-llms
공식 코드Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors
https://github.com/gchochla/cot-priors
공식 코드https://github.com/geode-project/ner-llm
공식 코드Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models
https://github.com/georgevern/lmcor
공식 코드Small Language Models Improve Giants by Rewriting Their Outputs
https://github.com/gewu-lab/llm_articulated_object_manipulation
공식 코드Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
https://github.com/ghupppp/memorysharingllm
공식 코드https://github.com/giilde/ft-vs-icl
공식 코드https://github.com/gimpong/CVPR25-Condenser
공식 코드Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning
https://github.com/gitter-lab/Assay2Mol
공식 코드Assay2Mol: large language model-based drug design using BioAssay context
https://github.com/gmago-leway/fewshotgec
공식 코드Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction
https://github.com/gmc-drl/awesome-metabbo
공식 코드Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
https://github.com/gmp007/propertyextractor
공식 코드https://github.com/google-deepmind/language_modeling_is_compression
공식 코드https://github.com/google-deepmind/thunnini
공식 코드Understanding Prompt Tuning and In-Context Learning via Meta-Learning
https://github.com/google-research/causallm_icl
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/self-organising-systems
공식 코드https://github.com/google-research/xtreme-up
공식 코드XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.