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 426–450 of 1135 papers

TitleStatusHype
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation—0
Better Instruction-Following Through Minimum Bayes Risk—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
LLaVA-Critic: Learning to Evaluate Multimodal Models—0
Video Instruction Tuning With Synthetic Data—0
MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI-SCE FrameworkCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Robin3D: Improving 3D Large Language Model via Robust Instruction TuningCode2
DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning DataCode2
The Perfect Blend: Redefining RLHF with Mixture of Judges—0
Revisiting the Superficial Alignment Hypothesis—0
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language ModelsCode1
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following Benchmark—0
Inference-Time Language Model Alignment via Integrated Value Guidance—0
Infer Human's Intentions Before Following Natural Language InstructionsCode1
Mitigating the Bias of Large Language Model EvaluationCode0
EventHallusion: Diagnosing Event Hallucinations in Video LLMsCode1
EAGLE: Towards Efficient Arbitrary Referring Visual Prompts Comprehension for Multimodal Large Language Models—0
FMDLlama: Financial Misinformation Detection based on Large Language ModelsCode0
MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object ScenariosCode1
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment BenchmarkingCode0
OmniBench: Towards The Future of Universal Omni-Language ModelsCode2
Archon: An Architecture Search Framework for Inference-Time TechniquesCode2
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and FeedbackCode1
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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