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 451–475 of 1135 papers

TitleStatusHype
Instruction Following without Instruction TuningCode1
Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language ModelsCode0
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language ModelsCode1
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation—0
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form PlanningCode1
CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks—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
SIFToM: Robust Spoken Instruction Following through Theory of Mind—0
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Emo-DPO: Controllable Emotional Speech Synthesis through Direct Preference Optimization—0
SFR-RAG: Towards Contextually Faithful LLMs—0
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?—0
ASFT: Aligned Supervised Fine-Tuning through Absolute LikelihoodCode3
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
LongGenBench: Benchmarking Long-Form Generation in Long Context LLMsCode1
Self-Judge: Selective Instruction Following with Alignment Self-EvaluationCode0
ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI SystemsCode3
Language Models Benefit from Preparation with Elicited Knowledge—0
Does Alignment Tuning Really Break LLMs' Internal Confidence?Code0
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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