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 151–200 of 1135 papers

TitleStatusHype
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language ModelsCode2
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist CollaborationCode2
mFollowIR: a Multilingual Benchmark for Instruction Following in RetrievalCode2
OmniBench: Towards The Future of Universal Omni-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
LLaVA-Plus: Learning to Use Tools for Creating Multimodal AgentsCode2
LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction TuningCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
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
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language ModelsCode2
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction TuningCode2
LLaSM: Large Language and Speech ModelCode2
Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language ModelsCode2
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward SystemsCode2
MM-IFEngine: Towards Multimodal Instruction FollowingCode2
MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific UnderstandingCode2
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMsCode2
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
EditWorld: Simulating World Dynamics for Instruction-Following Image EditingCode2
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuningCode2
Archon: An Architecture Search Framework for Inference-Time TechniquesCode2
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing DomainCode2
LLark: A Multimodal Instruction-Following Language Model for MusicCode2
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language ModelCode2
Learning to Decode Collaboratively with Multiple Language ModelsCode2
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil EngineeringCode2
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free LunchCode2
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMsCode2
FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual FusionCode2
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMsCode2
Dual-Space Knowledge Distillation for Large Language ModelsCode2
MiniLLM: Knowledge Distillation of Large Language ModelsCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Direct Preference Optimization of Video Large Multimodal Models from Language Model RewardCode2
DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning DataCode2
AutoDefense: Multi-Agent LLM Defense against Jailbreak AttacksCode2
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
Habitat: A Platform for Embodied AI ResearchCode2
BLSP-Emo: Towards Empathetic Large Speech-Language ModelsCode2
Large Language Model Instruction Following: A Survey of Progresses and ChallengesCode2
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal AlignmentCode2
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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