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
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language ModelsCode2
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended TasksCode2
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
mFollowIR: a Multilingual Benchmark for Instruction Following in RetrievalCode2
NavGPT: Explicit Reasoning in Vision-and-Language Navigation with 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
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
ExpertPrompting: Instructing Large Language Models to be Distinguished ExpertsCode2
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist CollaborationCode2
LLark: A Multimodal Instruction-Following Language Model for MusicCode2
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
EditWorld: Simulating World Dynamics for Instruction-Following Image EditingCode2
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
F-LMM: Grounding Frozen Large Multimodal ModelsCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMsCode2
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
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
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Dual-Space Knowledge Distillation for Large Language ModelsCode2
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language ModelCode2
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil EngineeringCode2
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning AbilitiesCode2
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMsCode2
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing DomainCode2
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-ExpertsCode2
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free LunchCode2
Learning to Decode Collaboratively with Multiple Language ModelsCode2
GeoChat: Grounded Large Vision-Language Model for Remote SensingCode2
MiniLLM: Knowledge Distillation of Large Language ModelsCode2
Direct Preference Optimization of Video Large Multimodal Models from Language Model RewardCode2
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMsCode2
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language ModelsCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning DataCode2
Rank1: Test-Time Compute for Reranking in Information RetrievalCode2
BLSP-Emo: Towards Empathetic Large Speech-Language ModelsCode2
Large Language Model Instruction Following: A Survey of Progresses and ChallengesCode2
GraphWiz: An Instruction-Following Language Model for Graph ProblemsCode2
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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