mlsanityData to 14 Oct 2026

Fairness of ChatGPT and the Role Of Explainable-Guided Prompts

arXiv 2307.11761 ↗First posted 14 Jul 2023cs.CL cs.AIcited by 13

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Abstract (arXiv)

Our research investigates the potential of Large-scale Language Models (LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary classification task. Our findings suggest that LLMs, when directed by judiciously designed prompts and supplemented with domain-specific knowledge, can parallel the performance of traditional Machine Learning (ML) models. Intriguingly, they achieve this with significantly less data-40 times less, utilizing merely 20 data points compared to the ML's 800. LLMs particularly excel in minimizing false positives and enhancing fairness, both being vital aspects of risk analysis. While our results did not surpass those of classical ML models, they underscore the potential of LLMs in analogous tasks, laying a groundwork for future explorations into harnessing the capabilities of LLMs in diverse ML tasks.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 15 of its 16 references that are in the map. First posted on arXiv 14 Jul 2023.

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15 of 16 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  4. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  5. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  6. 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 523
  7. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 518
  8. 2021A survey on datasets for fairness-aware machine learningcited by 157
  9. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
  10. 2023SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Supportcited by 46
  11. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  12. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
3 more
  1. 2023Fairness of ChatGPTcited by 20
  2. 2023Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classificationcited by 18
  3. 2023FaceChat: An Emotion-Aware Face-to-face Dialogue Frameworkcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 4; 2026: 2.

13 papers.

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  1. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 28
  2. 2024Navigating LLM Ethics: Advancements, Challenges, and Future Directionscited by 28
  3. 2024Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agendacited by 22
  4. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  5. 2025SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenderscited by 8
  6. 2023ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPTcited by 5
  7. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  8. 2024Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change Domaincited by 3
  9. 2024XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcarecited by 2
  10. 2025Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Tracescited by 1
  11. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
  12. 2025Comparing Credit Risk Estimates in the Gen-AI Eracited by 0
1 more
  1. 2026Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Makingcited by 0