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 151–175 of 1135 papers

TitleStatusHype
Learning to Decode Collaboratively with Multiple Language ModelsCode2
AutoDefense: Multi-Agent LLM Defense against Jailbreak AttacksCode2
Long-Context Language Modeling with Parallel Context EncodingCode2
GraphWiz: An Instruction-Following Language Model for Graph ProblemsCode2
Self-Distillation Bridges Distribution Gap in Language Model Fine-TuningCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task ArithmeticCode2
The Revolution of Multimodal Large Language Models: A SurveyCode2
Aligning Modalities in Vision Large Language Models via Preference Fine-tuningCode2
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended TasksCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language ModelsCode2
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing DomainCode2
Towards 3D Molecule-Text Interpretation in Language ModelsCode2
SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language ModelCode2
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
InFoBench: Evaluating Instruction Following Ability in Large Language ModelsCode2
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction TuningCode2
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-TuningCode2
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by StepCode2
LMDrive: Closed-Loop End-to-End Driving with Large Language ModelsCode2
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video UnderstandingCode2
GeoChat: Grounded Large Vision-Language Model for Remote SensingCode2
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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