SOTAVerified

Instruction Following

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.

Papers

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Showing 101–150 of 1135 papers

TitleStatusHype
MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMsCode2
NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language ModelsCode2
Precise Zero-Shot Dense Retrieval without Relevance LabelsCode2
mFollowIR: a Multilingual Benchmark for Instruction Following in RetrievalCode2
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language ModelsCode2
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist CollaborationCode2
Long-Context Language Modeling with Parallel Context EncodingCode2
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language ModelsCode2
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-ExpertsCode2
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection AttacksCode2
MM-IFEngine: Towards Multimodal Instruction FollowingCode2
BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language ModelsCode2
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular QuantizersCode2
MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World ControlCode2
Benchmarking Complex Instruction-Following with Multiple Constraints CompositionCode2
Autonomous Improvement of Instruction Following Skills via Foundation ModelsCode2
Aligning Modalities in Vision Large Language Models via Preference Fine-tuningCode2
ExpertPrompting: Instructing Large Language Models to be Distinguished ExpertsCode2
Open6DOR: Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based ApproachCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
LMDrive: Closed-Loop End-to-End Driving with Large Language ModelsCode2
AutoDefense: Multi-Agent LLM Defense against Jailbreak AttacksCode2
LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction TuningCode2
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-TuningCode2
CrystalFormer-RL: Reinforcement Fine-Tuning for Materials DesignCode2
LLaVA-Plus: Learning to Use Tools for Creating Multimodal AgentsCode2
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and ActionCode2
Critique Fine-Tuning: Learning to Critique is More Effective than Learning to ImitateCode2
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuningCode2
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
Learning to Decode Collaboratively with Multiple Language ModelsCode2
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free LunchCode2
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task ArithmeticCode2
LLark: A Multimodal Instruction-Following Language Model for MusicCode2
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing DomainCode2
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language ModelsCode2
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward SystemsCode2
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil EngineeringCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoIF (Llama3 70B)Inst-level loose-accuracy90.4—Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88—Unverified
3GPT-4Inst-level loose-accuracy85.37—Unverified
4PaLM 2 SInst-level loose-accuracy59.11—Unverified