https://github.com/elliottyan/refutebench
공식 코드RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
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/embeddings-benchmark/mteb
공식 코드https://github.com/emcie-co/parlant
공식 코드https://github.com/emilia113/omnigenbench
공식 코드OmniGenBench: A Benchmark for Omnipotent Multimodal Generation across 50+ Tasks
https://github.com/empathyang/tinyhelen
공식 코드https://github.com/ermongroup/teochat
공식 코드TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
https://github.com/ernie-research/cd-rlhf
공식 코드https://github.com/evo-eval/evoeval
공식 코드https://github.com/explanare/ood-prediction
공식 코드Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
https://github.com/facebookresearch/Multi-IF
공식 코드Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
https://github.com/facebookresearch/Replica-Dataset
공식 코드https://github.com/facebookresearch/codellama
공식 코드https://github.com/facebookresearch/habitat-sim
공식 코드https://github.com/facebookresearch/meta_secalign
공식 코드Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
https://github.com/facebookresearch/rlfh-gen-div
공식 코드Understanding the Effects of RLHF on LLM Generalisation and Diversity
https://github.com/fanqiwan/fuseai
공식 코드https://github.com/felipemaiapolo/sloth
공식 코드Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
https://github.com/fhudi/textgames
공식 코드TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning
https://github.com/findalexli/mllm-dpo
공식 코드https://github.com/findalexli/mllm-dpo/blob/main/README.MD
공식 코드https://github.com/for-ai/iterative-data-selection
공식 코드Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement
https://github.com/frankaging/dancing-in-chains
공식 코드Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models
https://github.com/frankroeder/lanro-gym
공식 코드Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections
https://github.com/freedomintelligence/acegpt
공식 코드https://github.com/freedomintelligence/sdak
공식 코드https://github.com/funaudiollm/cosyvoice
공식 코드https://github.com/geaming2002/ruler
공식 코드Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models
https://github.com/gengzezhou/navgpt
공식 코드NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models
https://github.com/gengzezhou/navgpt-2
공식 코드NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models
https://github.com/geox-lab/rs-gpt4v
공식 코드RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding
https://github.com/gililior/wild-if-eval-code
공식 코드https://github.com/gistvision/moca
공식 코드Factorizing Perception and Policy for Interactive Instruction Following
https://github.com/gkw0010/copesd
공식 코드https://github.com/gmkim-ai/promptkd
공식 코드PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
https://github.com/google-deepmind/pix2act
공식 코드From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
https://github.com/google-research/clevr_robot_env
공식 코드Language as an Abstraction for Hierarchical Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.