https://github.com/dynamofl/primeguard
공식 코드PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing
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/ebigelow/icl-random-binary
공식 코드https://github.com/ecielyang/auto-icl
공식 코드https://github.com/ecnu-text-computing/text-clustering-via-llm
공식 코드https://github.com/efficient-large-model/vila
공식 코드https://github.com/eit-nlp/2d-coordinate-system-for-icl
공식 코드Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
https://github.com/ekinakyurek/marc
공식 코드The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
https://github.com/ekkkkki/llm-replace-annotators-in-biomedical-text-mining
공식 코드https://github.com/eleutherai/gpt-neox
공식 코드ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
https://github.com/elliottyan/understand-icl-from-repetition
공식 코드https://github.com/eparascho/llms-for-explanations
공식 코드Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
https://github.com/epfl-nlp/scesc-llm-skill-extraction
공식 코드Rethinking Skill Extraction in the Job Market Domain using Large Language Models
https://github.com/ericelmoznino/explicit_implicit_icl
공식 코드Does learning the right latent variables necessarily improve in-context learning?
https://github.com/erogol/blagpt
공식 코드https://github.com/eshaant/x-insta
공식 코드Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment
https://github.com/eshnich/transformers-learn-causal-structure
공식 코드How Transformers Learn Causal Structure with Gradient Descent
https://github.com/eth-lre/llm_icl
공식 코드What Do Language Models Learn in Context? The Structured Task Hypothesis
https://github.com/eureka-research/Eureka
공식 코드Eureka: Human-Level Reward Design via Coding Large Language Models
https://github.com/exploration-lab/llm-for-clinical-report-generation-deidentification
공식 코드Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs
https://github.com/extensityai/benchmark
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
https://github.com/eydcao/vicon
공식 코드VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
https://github.com/f2-song/icdpo
공식 코드https://github.com/facebookresearch/MemoryMosaics
공식 코드https://github.com/facebookresearch/iclmlp
공식 코드A Closer Look at In-Context Learning under Distribution Shifts
https://github.com/facebookresearch/metaicl
공식 코드https://github.com/facebookresearch/segment-anything
공식 코드Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain
https://github.com/facebookresearch/worldsense
공식 코드WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models
https://github.com/fanglaosi/point-in-context
공식 코드Explore In-Context Learning for 3D Point Cloud Understanding
https://github.com/fanglaosi/skeleton-in-context
공식 코드Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
https://github.com/fangliancheng/tabgen-icl
공식 코드TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
https://github.com/fangyihao/hgot
공식 코드https://github.com/felixdenbreejen/tabforestpfn
공식 코드Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers
https://github.com/fewshot-goes-multilingual/slavic-incontext-learning
공식 코드Resources and Few-shot Learners for In-context Learning in Slavic Languages
https://github.com/firatcekinel/fctr
공식 코드Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
https://github.com/fla-org/flash-linear-attention
공식 코드https://github.com/flagopen/flagembedding
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.