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 611–620 of 1135 papers

TitleStatusHype
Active Reasoning in an Open-World Environment—0
ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance—0
ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models—0
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning—0
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming—0
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching—0
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning—0
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—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