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 101–110 of 1135 papers

TitleStatusHype
When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs—0
Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining—0
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents—0
BLEUBERI: BLEU is a surprisingly effective reward for instruction followingCode1
MergeBench: A Benchmark for Merging Domain-Specialized LLMsCode1
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation—0
Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation—0
HealthBench: Evaluating Large Language Models Towards Improved Human HealthCode7
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?Code0
A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language ModelsCode1
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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