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
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
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
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection AttacksCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMsCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil EngineeringCode2
Lion: Adversarial Distillation of Proprietary 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
Learning to Decode Collaboratively with Multiple Language ModelsCode2
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
LLark: A Multimodal Instruction-Following Language Model for MusicCode2
MiniLLM: Knowledge Distillation of Large Language ModelsCode2
Large Language Model Instruction Following: A Survey of Progresses and ChallengesCode2
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward SystemsCode2
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-ExpertsCode2
#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-accuracy90.4—Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88—Unverified
3GPT-4Inst-level loose-accuracy85.37—Unverified
4PaLM 2 SInst-level loose-accuracy59.11—Unverified