https://github.com/mlgroupjlu/structflowbench
공식 코드StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following
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/mlvlab/llamo
공식 코드https://github.com/mnoukhov/async_rlhf
공식 코드Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
https://github.com/mozhu621/SGT
공식 코드LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs
https://github.com/mozhu621/longgenbench
공식 코드LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs
https://github.com/mshukor/EvALign-ICL
공식 코드https://github.com/mtkresearch/mr-models
공식 코드https://github.com/multimodal-art-projection/omnibench
공식 코드OmniBench: Towards The Future of Universal Omni-Language Models
https://github.com/neulab/gemini-benchmark
공식 코드https://github.com/nlpxucan/wizardlm
공식 코드WizardLM: Empowering Large Language Models to Follow Complex Instructions
https://github.com/now-join-us/cit-llm-routing
공식 코드Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing
https://github.com/nuochenpku/Graph-Reasoning-LLM
공식 코드GraphWiz: An Instruction-Following Language Model for Graph Problems
https://github.com/nusnlp/Self-J
공식 코드Self-Judge: Selective Instruction Following with Alignment Self-Evaluation
https://github.com/nusnlp/prs
공식 코드Preference-Guided Reflective Sampling for Aligning Language Models
https://github.com/nvlabs/lita
공식 코드https://github.com/oddqueue/self-judge
공식 코드https://github.com/ody-trek/llava-pose
공식 코드LLaVA-Pose: Enhancing Human Pose and Action Understanding via Keypoint-Integrated Instruction Tuning
https://github.com/ofa-sys/air-bench
공식 코드AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
https://github.com/ofa-sys/ditto
공식 코드https://github.com/ofa-sys/diverseevol
공식 코드Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
https://github.com/ofa-sys/expertllama
공식 코드ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
https://github.com/ofa-sys/gsm8k-screl
공식 코드How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
https://github.com/ofa-sys/instag
공식 코드#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
https://github.com/open-compass/botchat
공식 코드BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues
https://github.com/open-compass/t-eval
공식 코드T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
https://github.com/open-compass/vlmevalkit
공식 코드https://github.com/open-mmlab/multimodal-gpt
공식 코드MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
https://github.com/openai/simple-evals
공식 코드HealthBench: Evaluating Large Language Models Towards Improved Human Health
https://github.com/opendilab/lmdrive
공식 코드LMDrive: Closed-Loop End-to-End Driving with Large Language Models
https://github.com/opengvlab/chartast
공식 코드https://github.com/opengvlab/llama-adapter
공식 코드LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/opengvlab/multi-modality-arena
공식 코드LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
https://github.com/opening-up-chatgpt/opening-up-chatgpt.github.io
공식 코드https://github.com/openlamm/lamm
공식 코드https://github.com/openlmlab/f-eval
공식 코드F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods
https://github.com/openlmlab/leval
공식 코드L-Eval: Instituting Standardized Evaluation for Long Context Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.