mlsanityData to 14 Oct 2026

Grading Conversational Responses Of Chatbots

arXiv 2303.12038 ↗First posted 1 Feb 2023cs.CL cs.LGcited by 1

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Abstract (arXiv)

Chatbots have long been capable of answering basic questions and even responding to obscure prompts, but recently their improvements have been far more significant. Modern chatbots like Open AIs ChatGPT3 not only have the ability to answer basic questions but can write code and movie scripts and imitate well-known people. In this paper, we analyze ChatGPTs' responses to various questions from a dataset of queries from the popular Quora forum. We submitted sixty questions to ChatGPT and scored the answers based on three industry-standard metrics for grading machine translation: BLEU, METEOR, and ROUGE. These metrics allow us to compare the machine responses with the most upvoted human answer to the same question to assess ChatGPT's ability to submit a humanistic reply. The results showed that while the responses and translation abilities of ChatGPT are remarkable, they still fall short of what a typical human reaction would be.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 1 Feb 2023.

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8 of 14 references.

References · 8

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022Natural Language Interfaces to Datacited by 14
  3. 2023UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systemscited by 11
  4. 2023Chatbots in a Honeypot Worldcited by 9
  5. 2022Chatbots in a Botnet Worldcited by 8
  6. 2022Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecturecited by 7
  7. 2022Natural Language Processing in Customer Service: A Systematic Reviewcited by 4
  8. 2023Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippetscited by 3

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