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Instruction Following
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Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AFeng-x/Draw-and-Understand
공식 코드Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
https://github.com/AdaBit-AI/parameter_efficient_instruction_tuning
공식 코드https://github.com/Alibaba-NLP/EcomGPT
공식 코드EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
https://github.com/AmritaBh/zero-shot-llm-counterfactual
공식 코드Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model Evaluation
https://github.com/DAMO-NLP-SG/SeaExam
공식 코드SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
https://github.com/Dong-Jie-Chen/RCL
공식 코드Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning
https://github.com/EIT-NLP/InfoSearch
공식 코드Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models
https://github.com/EVOKE-LMM/EVOKE
공식 코드When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways
https://github.com/Euphoria16/DocMark
공식 코드Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
https://github.com/FedericoAureliano/eudoxus
공식 코드https://github.com/FunAudioLLM/SenseVoice
공식 코드https://github.com/Genius1237/babyai
공식 코드Automated curriculum generation for Policy Gradients from Demonstrations
https://github.com/Glaciohound/Multi-Turn-Instruct
공식 코드Can Language Models Follow Multiple Turns of Entangled Instructions?
https://github.com/Hojjat-Mokhtarabadi/FarsInstruct
공식 코드Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training Approach
https://github.com/Homagn/MOVILAN
공식 코드https://github.com/HomeroRR/rmm
공식 코드https://github.com/IAAR-Shanghai/Meta-Chunking
공식 코드Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception
https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
공식 코드https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V
공식 코드ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
https://github.com/InternLM/opencompass
공식 코드https://github.com/IrohXu/MpoxVLM
공식 코드MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus Infection
https://github.com/JackYFL/EmoLA
공식 코드https://github.com/Joe-Hall-Lee/Debias
공식 코드https://github.com/JourdanL/parareval
공식 코드Identifying Reliable Evaluation Metrics for Scientific Text Revision
https://github.com/LLaVA-VL/LLaVA-Plus-Codebase
공식 코드LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
https://github.com/LinesHogan/Token-Efficient-Leverage-Learning
공식 코드https://github.com/MLD3/steerability
공식 코드A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs
https://github.com/MacavityT/REF-VLM
공식 코드REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding
https://github.com/McGill-NLP/polytropon
공식 코드https://github.com/NJU-LHRS/LHRS-Bot
공식 코드https://github.com/OPPOMKLab/u-LLaVA
공식 코드u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
https://github.com/OpenM3D/M3DBench
공식 코드M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts
https://github.com/OpenRobotLab/Grounded_3D-LLM
공식 코드https://github.com/Owen-Oertell/rlcm
공식 코드RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
https://github.com/Pints-AI/1.5-Pints
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.