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

Showing 126150 of 1135 papers

TitleStatusHype
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-TuningCode2
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
LLaSM: Large Language and Speech ModelCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection AttacksCode2
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil EngineeringCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
Learning to Decode Collaboratively with Multiple Language ModelsCode2
Direct Preference Optimization of Video Large Multimodal Models from Language Model RewardCode2
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
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
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal AlignmentCode2
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward SystemsCode2
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language ModelsCode2
Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language ModelsCode2
InFoBench: Evaluating Instruction Following Ability in Large Language ModelsCode2
How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise GradientsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoIF (Llama3 70B)Inst-level loose-accuracy90.4Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88Unverified
3GPT-4Inst-level loose-accuracy85.37Unverified
4PaLM 2 SInst-level loose-accuracy59.11Unverified