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 441–450 of 1135 papers

TitleStatusHype
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code GeneratorsCode1
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy InstructionsCode1
IDA-Bench: Evaluating LLMs on Interactive Guided Data AnalysisCode1
Is In-Context Learning Sufficient for Instruction Following in LLMs?Code1
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
Distilling Internet-Scale Vision-Language Models into Embodied Agents—0
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning—0
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models—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