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 251–300 of 1135 papers

TitleStatusHype
Factorizing Perception and Policy for Interactive Instruction FollowingCode1
Instruct and Extract: Instruction Tuning for On-Demand Information ExtractionCode1
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Instruction-Following Agents with Multimodal TransformerCode1
AllenAct: A Framework for Embodied AI ResearchCode1
M-IFEval: Multilingual Instruction-Following EvaluationCode1
CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM WisdomCode1
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank AdaptationCode1
MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object ScenariosCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
Back to the Future: Towards Explainable Temporal Reasoning with Large Language ModelsCode1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Inferring Rewards from Language in ContextCode1
Infer Human's Intentions Before Following Natural Language InstructionsCode1
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language NavigationCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMsCode1
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
Improving Translation Faithfulness of Large Language Models via Augmenting InstructionsCode1
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMsCode1
Incentivizing Reasoning for Advanced Instruction-Following of Large Language ModelsCode1
CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal DissectionCode1
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction TuningCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction FollowingCode1
MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI-SCE FrameworkCode1
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation ExtractorsCode1
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language ModelsCode1
IHEval: Evaluating Language Models on Following the Instruction HierarchyCode1
Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied AgentsCode1
Constraint Back-translation Improves Complex Instruction Following of Large Language ModelsCode1
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language ModelsCode1
Making Large Language Models Better Data CreatorsCode1
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn InteractionsCode1
LoGU: Long-form Generation with Uncertainty ExpressionsCode1
Combining Modular Skills in Multitask LearningCode1
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMsCode1
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction DataCode1
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and GenerationCode1
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form PlanningCode1
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code GenerationCode1
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language ModelsCode1
Hybrid Alignment Training for Large Language ModelsCode1
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMsCode1
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal ModelsCode1
A Survey on Data Selection for LLM Instruction TuningCode1
Alexa Arena: A User-Centric Interactive Platform for Embodied AICode1
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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