mlsanityData to 14 Oct 2026

Loquacity and Visible Emotion: ChatGPT as a Policy Advisor

arXiv 2309.03595 ↗First posted 7 Sep 2023cs.CL cs.HCcited by 0

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Abstract (arXiv)

ChatGPT, a software seeking to simulate human conversational abilities, is attracting increasing attention. It is sometimes portrayed as a groundbreaking productivity aid, including for creative work. In this paper, we run an experiment to assess its potential in complex writing tasks. We ask the software to compose a policy brief for the Board of the Bank of Italy. We find that ChatGPT can accelerate workflows by providing well-structured content suggestions, and by producing extensive, linguistically correct text in a matter of seconds. It does, however, require a significant amount of expert supervision, which partially offsets productivity gains. If the app is used naively, output can be incorrect, superficial, or irrelevant. Superficiality is an especially problematic limitation in the context of policy advice intended for high-level audiences.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 7 Sep 2023.

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11 of 37 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  6. 2023BloombergGPT: A Large Language Model for Financecited by 939
  7. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  8. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  9. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
  10. 2023Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Modelscited by 129
  11. 2023Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failurecited by 10

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