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 776–800 of 1135 papers

TitleStatusHype
SIFToM: Robust Spoken Instruction Following through Theory of Mind—0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models—0
SFR-RAG: Towards Contextually Faithful LLMs—0
Emo-DPO: Controllable Emotional Speech Synthesis through Direct Preference Optimization—0
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?—0
Keypoints-Integrated Instruction-Following Data Generation for Enhanced Human Pose Understanding in Multimodal Models—0
AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting LLMsCode0
RNR: Teaching Large Language Models to Follow Roles and Rules—0
Leveraging LLMs for Influence Path Planning in Proactive Recommendation—0
Continual Skill and Task Learning via Dialogue—0
Prompt Baking—0
Self-Judge: Selective Instruction Following with Alignment Self-EvaluationCode0
Language Models Benefit from Preparation with Elicited Knowledge—0
Does Alignment Tuning Really Break LLMs' Internal Confidence?Code0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis—0
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction TuningCode0
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora—0
Preference-Guided Reflective Sampling for Aligning Language ModelsCode0
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation—0
Can Large Language Models Understand Symbolic Graphics Programs?—0
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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