mlsanityData to 13 Oct 2026

Assessing Political Prudence of Open-domain Chatbots

arXiv 2106.06157 ↗First posted 11 Jun 2021cs.CL cs.AIcited by 20

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Abstract (arXiv)

Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.

Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 11 Jun 2021.

Builds on

18 of 28 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
  3. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,676
  4. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,249
  5. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  6. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 824
  7. 2020Recipes for building an open-domain chatbotcited by 593
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  10. 2020Recipes for Safety in Open-domain Chatbotscited by 170
  11. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
  12. 2019Does Gender Matter? Towards Fairness in Dialogue Systemscited by 89
6 more
  1. 2019ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisonscited by 83
  2. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  3. 2021Towards Few-Shot Fact-Checking via Perplexitycited by 63
  4. 2020The Adapter-Bot: All-In-One Controllable Conversational Modelcited by 38
  5. 2019A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agentscited by 12
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 8; 2024: 1; 2025: 4; 2026: 1.

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  1. 2023MEGA: Multilingual Evaluation of Generative AIcited by 293
  2. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 260
  3. 2023Language Model Behavior: A Comprehensive Surveycited by 92
  4. 2024Measuring Political Bias in Large Language Models: What Is Said and How It Is Saidcited by 89
  5. 2022COLD: A Benchmark for Chinese Offensive Language Detectioncited by 75
  6. 2021On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmarkcited by 73
  7. 2021Few-Shot Bot: Prompt-Based Learning for Dialogue Systemscited by 64
  8. 2025Linear Representations of Political Perspective Emerge in Large Language Modelscited by 31
  9. 2022NeuS: Neutral Multi-News Summarization for Mitigating Framing Biascited by 26
  10. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  11. 2023Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvementscited by 14
  12. 2023Leveraging Implicit Feedback from Deployment Data in Dialoguecited by 14
8 more
  1. 2022Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generationcited by 8
  2. 2025Framing Political Bias in Multilingual LLMs Across Pakistani Languagescited by 5
  3. 2023P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Modelscited by 4
  4. 2026Bias Beyond Borders: Political Ideology Evaluation and Steering in Multilingual LLMscited by 4
  5. 2023Alquist 5.0: Dialogue Trees Meet Generative Models. A Novel Approach for Enhancing SocialBot Conversationscited by 1
  6. 2025Don't Change My View: Ideological Bias Auditing in Large Language Modelscited by 1
  7. 2021Taming the Beast: Learning to Control Neural Conversational Modelscited by 0
  8. 2025Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AIcited by 0