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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 1135 papers

TitleStatusHype
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