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 311–320 of 1135 papers

TitleStatusHype
Large Language Models as Evaluators for Recommendation ExplanationsCode1
Is In-Context Learning Sufficient for Instruction Following in LLMs?Code1
Instruction-Guided Visual MaskingCode1
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn InteractionsCode1
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source DataCode1
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction TuningCode1
RecGPT: Generative Pre-training for Text-based RecommendationCode1
The Instruction Hierarchy: Training LLMs to Prioritize Privileged InstructionsCode1
Facial Affective Behavior Analysis with Instruction TuningCode1
Evaluating LLMs at Detecting Errors in LLM ResponsesCode1
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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