https://github.com/kuleshov-group/llmtools
공식 코드ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Papers with Code 아카이브
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/kuleshov-group/modulora-experiment
공식 코드ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
https://github.com/kwai-keye/keye
공식 코드https://github.com/langmanbusi/insvie
공식 코드InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset Construction
https://github.com/leezekun/adv-instruct-eval
공식 코드Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection
https://github.com/leezekun/instruction-following-robustness-eval
공식 코드Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection
https://github.com/leezekun/mmsci
공식 코드MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding
https://github.com/lhao499/instructrl
공식 코드https://github.com/lhydave/ida-bench
공식 코드IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis
https://github.com/li-z-q/url
공식 코드URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
https://github.com/lianjiatech/belle
공식 코드https://github.com/lifebench/lifebench
공식 코드LIFEBench: Evaluating Length Instruction Following in Large Language Models
https://github.com/lightblue-tech/M-IFEval
공식 코드https://github.com/lil-lab/cb-analysis
공식 코드Analysis of Language Change in Collaborative Instruction Following
https://github.com/lil-lab/cb2
공식 코드CB2: Collaborative Natural Language Interaction Research Platform
https://github.com/lil-lab/clif_cb
공식 코드Continual Learning for Instruction Following from Realtime Feedback
https://github.com/lil-lab/drif
공식 코드Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
https://github.com/lilakk/bleuberi
공식 코드BLEUBERI: BLEU is a surprisingly effective reward for instruction following
https://github.com/lilianngweta/aligners
공식 코드https://github.com/lilianngweta/aligners-and-inspectors
공식 코드https://github.com/lin-rany/codeIF
공식 코드https://github.com/linjc16/Rec-R1
공식 코드https://github.com/linksoul-ai/llasm
공식 코드https://github.com/lirenhao1997/coevol
공식 코드CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation
https://github.com/listentm/crowdselect
공식 코드CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdom
https://github.com/liturte/FALCON
공식 코드FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system
https://github.com/liuliuyuan6/POCL
공식 코드https://github.com/liuzuyan/elasticcache
공식 코드Efficient Inference of Vision Instruction-Following Models with Elastic Cache
https://github.com/livebench/livebench
공식 코드LiveBench: A Challenging, Contamination-Limited LLM Benchmark
https://github.com/lizhaoliu-lec/cg-vlm
공식 코드Contrastive Vision-Language Alignment Makes Efficient Instruction Learner
https://github.com/lizhouf/nphardeval4v
공식 코드NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
https://github.com/llm-as-dataset-analyst/SSDLLM
공식 코드LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model
https://github.com/lm-playpen/playpen
공식 코드Playpen: An Environment for Exploring Learning Through Conversational Interaction
https://github.com/lm-sys/fastchat
공식 코드LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/lostwolves/IFShip
공식 코드https://github.com/lranaldii/crossalpaca
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.