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 251–260 of 1135 papers

TitleStatusHype
Language Imbalance Driven Rewarding for Multilingual Self-improvingCode1
Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-TranslationCode1
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language NavigationCode1
Lana: A Language-Capable Navigator for Instruction Following and GenerationCode1
Jatmo: Prompt Injection Defense by Task-Specific FinetuningCode1
CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal DissectionCode1
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction TuningCode1
Is In-Context Learning Sufficient for Instruction Following in LLMs?Code1
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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