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This paper introduces RuleArena, a novel and challenging benchmark designed to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning.
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Chain-of-thought prompting elicits reasoning in large language models
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023 · 2023
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Nphardeval: Dynamic benchmark on reasoning ability of large language models via complexity classes
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Alpacaeval: An automatic evaluator of instruction-following models
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Can large language models understand real-world complex instructions?
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Evaluating large language models on controlled generation tasks
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
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Larger language models do in-context learning differently
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Disentangling logic: The role of context in large language model reasoning capabilities
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Fine-tuning and utilization methods of domain-specific llms
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Followbench: A multi-level fine-grained constraints following benchmark for large language models
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Qwen2.5: A party of foundation models
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Symbolic working memory enhances language models for complex rule application
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Benchmarking complex instruction-following with multiple constraints composition
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AKEW: Assessing knowledge editing in the wild
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Pride and prejudice: LLM amplifies self-bias in self-refinement
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Xiang Zhang and Dujian Ding. 2024 · 2024
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TRAD: Enhancing llm agents with step-wise thought retrieval and aligned decision
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