title: Bot or Human? Detecting ChatGPT Imposters with A Single Question
summary: Large language models like ChatGPT have recently demonstrated impressive
  capabilities in natural language understanding and generation, enabling various
  applications including translation, essay writing, and chit-chatting. However,
  there is a concern that they can be misused for malicious purposes, such as
  fraud or denial-of-service attacks. Therefore, it is crucial to develop methods
  for detecting whether the party involved in a conversation is a bot or a human.
  In this paper, we propose a framework named FLAIR, Finding Large language model
  Authenticity via a single Inquiry and Response, to detect conversational bots
  in an online manner. Specifically, we target a single question scenario that
  can effectively differentiate human users from bots. The questions are divided
  into two categories: those that are easy for humans but difficult for bots
  (e.g., counting, substitution, positioning, noise filtering, and ASCII art),
  and those that are easy for bots but difficult for humans (e.g., memorization
  and computation). Our approach shows different strengths of these questions in
  their effectiveness, providing a new way for online service providers to
  protect themselves against nefarious activities and ensure that they are
  serving real users. We open-sourced our dataset on
  https://github.com/hongwang600/FLAIR and welcome contributions from the
  community to enrich such detection datasets.
paper_authors._id: 645da115ce72244df7b4749e
paper_authors.name: Hong Wang
paper_authors._id: 645da115ce72244df7b4749f
paper_authors.name: Xuan Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-02T08:59:03.218Z
paper_authors._id: 659d98feec9f8f337f145ec4
paper_authors.avatarUrl: /avatars/7de3bea0f203326e78916882655b5d55.svg
paper_authors.fullname: xuan luo
paper_authors.type: user
paper_authors.user: xuan-luo
paper_authors._id: 645da115ce72244df7b474a0
paper_authors.name: Weizhi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-16T09:55:52.832Z
paper_authors._id: 63d34004b734eaa4d4faeccf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63d34004b734eaa4d4faeccf/aH7F-xn4PMQjHHcvL-0vs.jpeg
paper_authors.fullname: Weizhi Wang
paper_authors.type: user
paper_authors.user: weizhiwang
paper_authors._id: 645da115ce72244df7b474a1
paper_authors.name: Xifeng Yan
paper_title: Bot or Human? Detecting ChatGPT Imposters with A Single Question
paper_summary: Large language models like ChatGPT have recently demonstrated impressive
  capabilities in natural language understanding and generation, enabling various
  applications including translation, essay writing, and chit-chatting. However,
  there is a concern that they can be misused for malicious purposes, such as
  fraud or denial-of-service attacks. Therefore, it is crucial to develop methods
  for detecting whether the party involved in a conversation is a bot or a human.
  In this paper, we propose a framework named FLAIR, Finding Large language model
  Authenticity via a single Inquiry and Response, to detect conversational bots
  in an online manner. Specifically, we target a single question scenario that
  can effectively differentiate human users from bots. The questions are divided
  into two categories: those that are easy for humans but difficult for bots
  (e.g., counting, substitution, positioning, noise filtering, and ASCII art),
  and those that are easy for bots but difficult for humans (e.g., memorization
  and computation). Our approach shows different strengths of these questions in
  their effectiveness, providing a new way for online service providers to
  protect themselves against nefarious activities and ensure that they are
  serving real users. We open-sourced our dataset on
  https://github.com/hongwang600/FLAIR and welcome contributions from the
  community to enrich such detection datasets.
paper_ai_summary: FLAIR framework detects conversational bots online using targeted questions to differentiate between human and artificial responses.
paper_ai_keywords: large language models conversational bots authentication differentiation counting substitution positioning noise filtering ASCII art memorization computation