Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals

• URL: http://arxiv.org/abs/2301.01743v1
• Date: Sun, 1 Jan 2023 03:04:04 GMT
• Title: Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals
• Authors: David Noever, Forrest McKee
• Abstract summary: New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to the answerer.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to the answerer. The main empirical result establishes that this generation of chat applications can guess random object names in fewer than twenty questions (average, 12) and correctly guess 94% of the time across sixteen different experimental setups. The research introduces four novel cases where the chatbot fields the questions, asks the questions, both question-answer roles, and finally tries to guess appropriate contextual emotions. One task that humans typically fail but trained chat applications complete involves playing bilingual games of twenty questions (English answers to Spanish questions). Future variations address direct problem-solving using a similar inquisitive format to arrive at novel outcomes deductively, such as patentable inventions or combination thinking. Featured applications of this dialogue format include complex protein designs, neuroscience metadata, and child development educational materials.

Related papers

• How to Engage Your Readers? Generating Guiding Questions to Promote Active Reading [60.19226384241482]
  We introduce GuidingQ, a dataset of 10K in-text questions from textbooks and scientific articles. We explore various approaches to generate such questions using language models. We conduct a human study to understand the implication of such questions on reading comprehension.
  arXiv  Detail & Related papers  (2024-07-19T13:42:56Z)
• Which questions should I answer? Salience Prediction of Inquisitive Questions [118.097974193544]
  We show that highly salient questions are empirically more likely to be answered in the same article. We further validate our findings by showing that answering salient questions is an indicator of summarization quality in news.
  arXiv  Detail & Related papers  (2024-04-16T21:33:05Z)
• A novel interface for adversarial trivia question-writing [2.132441341551259]
  We introduce an interface for collecting adversarial human-written trivia questions. Our interface is aimed towards question writers and players of Quiz Bowl. To incentivize usage, a suite of machine learning-based tools in our interface assist humans in writing questions.
  arXiv  Detail & Related papers  (2024-03-12T02:37:24Z)
• Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions [95.92276099234344]
  We present a new state-of-the-art for answering ambiguous questions that exploits a database of unambiguous questions generated from Wikipedia. Our method improves performance by 15% on recall measures and 10% on measures which evaluate disambiguating questions from predicted outputs.
  arXiv  Detail & Related papers  (2023-08-16T20:23:16Z)
• NewsDialogues: Towards Proactive News Grounded Conversation [72.10055780635625]
  We propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news. To further develop this novel task, we collect a human-to-human Chinese dialogue dataset tsNewsDialogues, which includes 1K conversations with a total of 14.6K utterances.
  arXiv  Detail & Related papers  (2023-08-12T08:33:42Z)
• Adding guardrails to advanced chatbots [5.203329540700177]
  Launch of ChatGPT in November 2022 has ushered in a new era of AI. There are already concerns that humans may be replaced by chatbots for a variety of jobs. These biases may cause significant harm and/or inequity toward different subpopulations.
  arXiv  Detail & Related papers  (2023-06-13T02:23:04Z)
• Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard [68.8204255655161]
  We use 30 questions that are clear, without any ambiguities, fully described with plain text only, and have a unique, well defined correct answer. The answers are recorded and discussed, highlighting their strengths and weaknesses. It was found that ChatGPT-4 outperforms ChatGPT-3.5 in both sets of questions.
  arXiv  Detail & Related papers  (2023-05-30T11:18:05Z)
• What Types of Questions Require Conversation to Answer? A Case Study of AskReddit Questions [16.75969771718778]
  We aim to push the boundaries of conversational systems by examining the types of nebulous, open-ended questions that can best be answered through conversation. We sampled 500 questions from one million open-ended requests posted on AskReddit, and then recruited online crowd workers to answer eight inquiries about these questions. We found that the issues people believe require conversation to resolve satisfactorily are highly social and personal.
  arXiv  Detail & Related papers  (2023-03-30T21:05:22Z)
• Conversational QA Dataset Generation with Answer Revision [2.5838973036257458]
  We introduce a novel framework that extracts question-worthy phrases from a passage and then generates corresponding questions considering previous conversations. Our framework revises the extracted answers after generating questions so that answers exactly match paired questions.
  arXiv  Detail & Related papers  (2022-09-23T04:05:38Z)
• ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ) [64.60303062063663]
  This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ) The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020.
  arXiv  Detail & Related papers  (2020-09-23T19:48:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.