title: Reflection-Bench: probing AI intelligence with reflection
summary: The ability to adapt beliefs or behaviors in response to unexpected outcomes,
  reflection, is fundamental to intelligent systems' interaction with the world.
  From a cognitive science perspective, this serves as a core principle of
  intelligence applicable to both human and AI systems. To address the debate on
  the intelligence of large language models (LLMs), we propose Reflection-Bench,
  a comprehensive benchmark comprising 7 tasks spanning core cognitive functions
  crucial for reflection, including perception, memory, belief updating,
  decision-making, prediction, counterfactual thinking, and meta-reflection. We
  evaluate the performances of 13 prominent LLMs such as OpenAI o1, GPT-4, Claude
  3.5 Sonnet, etc. The results indicate that current LLMs still lack satisfactory
  reflection ability. We discuss the underlying causes of these results and
  suggest potential avenues for future research. In conclusion, Reflection-Bench
  offers both evaluation tools and inspiration for developing AI capable of
  reliably interacting with the environment. Our data and code are available at
  https://github.com/YabYum/ReflectionBench.
paper_authors._id: 671f35fc2750014020ef8e8f
paper_authors.name: Lingyu Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-28T08:51:28.542Z
paper_authors._id: 65c1d9413601dac1372b0ffe
paper_authors.avatarUrl: /avatars/109f2af09a29b16a884b77c930bb7f48.svg
paper_authors.fullname: Lingyu Li
paper_authors.type: user
paper_authors.user: LingyuLi
paper_authors._id: 671f35fc2750014020ef8e90
paper_authors.name: Yixu Wang
paper_authors._id: 671f35fc2750014020ef8e91
paper_authors.name: Haiquan Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:45:27.152Z
paper_authors._id: 64fecf8dd30d99e75e0a557c
paper_authors.avatarUrl: /avatars/6f6ba57711f7717bcab8e3db05a849cb.svg
paper_authors.fullname: Haiquan Zhao
paper_authors.type: user
paper_authors.user: haidequanbu
paper_authors._id: 671f35fc2750014020ef8e92
paper_authors.name: Shuqi Kong
paper_authors._id: 671f35fc2750014020ef8e93
paper_authors.name: Yan Teng
paper_authors._id: 671f35fc2750014020ef8e94
paper_authors.name: Chunbo Li
paper_authors._id: 671f35fc2750014020ef8e95
paper_authors.name: Yingchun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:45:58.257Z
paper_authors._id: 66f108310ea83b350d0d7411
paper_authors.avatarUrl: /avatars/93a68a13b016a8ae81a3453840addfc3.svg
paper_authors.fullname: Yingchun WANG
paper_authors.type: user
paper_authors.user: DaenerysW
paper_title: Reflection-Bench: probing AI intelligence with reflection
paper_summary: The ability to adapt beliefs or behaviors in response to unexpected outcomes,
  reflection, is fundamental to intelligent systems' interaction with the world.
  From a cognitive science perspective, this serves as a core principle of
  intelligence applicable to both human and AI systems. To address the debate on
  the intelligence of large language models (LLMs), we propose Reflection-Bench,
  a comprehensive benchmark comprising 7 tasks spanning core cognitive functions
  crucial for reflection, including perception, memory, belief updating,
  decision-making, prediction, counterfactual thinking, and meta-reflection. We
  evaluate the performances of 13 prominent LLMs such as OpenAI o1, GPT-4, Claude
  3.5 Sonnet, etc. The results indicate that current LLMs still lack satisfactory
  reflection ability. We discuss the underlying causes of these results and
  suggest potential avenues for future research. In conclusion, Reflection-Bench
  offers both evaluation tools and inspiration for developing AI capable of
  reliably interacting with the environment. Our data and code are available at
  https://github.com/YabYum/ReflectionBench.
paper_ai_summary: Reflection-Bench is a benchmark for evaluating the reflection abilities of large language models across various cognitive functions, highlighting their current limitations and future research directions.
paper_ai_keywords: large language models LLMs Reflection-Bench perception memory belief updating decision-making prediction counterfactual thinking meta-reflection