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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/groundlight/vlm-visual-demonstrations
공식 코드Can Vision Language Models Learn from Visual Demonstrations of Ambiguous Spatial Reasoning?
https://github.com/gszfwsb/Data-Whisperer
공식 코드https://github.com/gucioopielka/concept_vectors
공식 코드Analogical Reasoning Inside Large Language Models: Concept Vectors and the Limits of Abstraction
https://github.com/gunny97/DRAFT
공식 코드DRAFT: Dense Retrieval Augmented Few-shot Topic classifier Framework
https://github.com/gyauney/max-random-baseline
공식 코드https://github.com/gyauney/stronger-random-baselines
공식 코드https://github.com/hanklee97121/bear_bgp_event_analysis
공식 코드https://github.com/haoheliu/AudioLDM2
공식 코드AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
https://github.com/haozhezhao/mic
공식 코드MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
https://github.com/harmonbhasin/curriculum_learning_icl
공식 코드https://github.com/harvey-fin/icl-estimate
공식 코드Estimating Large Language Model Capabilities without Labeled Test Data
https://github.com/hc495/Hidden_Calibration
공식 코드Token-based Decision Criteria Are Suboptimal in In-context Learning
https://github.com/hc495/ICL_Circuit
공식 코드Revisiting In-context Learning Inference Circuit in Large Language Models
https://github.com/hc495/staicc
공식 코드StaICC: Standardized Evaluation for Classification Task in In-context Learning
https://github.com/heaven-zhw/llm-on-tabfact
공식 코드https://github.com/hengrui-gu/t2iknowledgeediting
공식 코드https://github.com/hewei2001/Self-Demos
공식 코드Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models
https://github.com/hhkz/noisyicl
공식 코드NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning
https://github.com/hitsz-tmg/explaincpe
공식 코드ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
https://github.com/hitsz-tmg/gemel
공식 코드https://github.com/hitsz-tmg/icl-state-vector
공식 코드In-Context Learning State Vector with Inner and Momentum Optimization
https://github.com/hitsz-tmg/multimodal-in-context-tuning
공식 코드A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
https://github.com/hitsz-tmg/pica
공식 코드Take Off the Training Wheels Progressive In-Context Learning for Effective Alignment
https://github.com/hiyouga/llama-factory
공식 코드Revealing the Parallel Multilingual Learning within Large Language Models
https://github.com/hkuds/flashst
공식 코드FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
https://github.com/hkunlp/batch-prompting
공식 코드Batch Prompting: Efficient Inference with Large Language Model APIs
https://github.com/hkunlp/diffullama
공식 코드Scaling Diffusion Language Models via Adaptation from Autoregressive Models
https://github.com/hkunlp/icl-ceil
공식 코드https://github.com/hkunlp/icl-selective-annotation
공식 코드Selective Annotation Makes Language Models Better Few-Shot Learners
https://github.com/hlzhang109/icl-calibration
공식 코드https://github.com/hlzhang109/lmlp
공식 코드Evaluating Step-by-Step Reasoning through Symbolic Verification
https://github.com/hoezey/vlm-compositionality
공식 코드In-Context Learning Improves Compositional Understanding of Vision-Language Models
https://github.com/holistic-ai/HEARTS-Text-Stereotype-Detection
공식 코드HEARTS: A Holistic Framework for Explainable, Sustainable and Robust Text Stereotype Detection
https://github.com/holistic-ai/THaMES
공식 코드THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
https://github.com/hongyuanluke/chain-of-dictionary
공식 코드Chain-of-Dictionary Prompting Elicits Translation in Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.