Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses

• URL: http://arxiv.org/abs/2404.14454v2
• Date: Sun, 2 Jun 2024 22:42:00 GMT
• Title: Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses
• Authors: Yousef Khan, Ahmed Abdeen Hamed,
• Abstract summary: The research aims to evaluate ChatGPT's reasoning capabilities, emphasizing its potential to process rules and provide explanations for screening recommendations. The methodology employs a supervised prompt-engineering approach to enforce detailed explanations for ChatGPT's recommendations. Findings highlight ChatGPT's promising capacity in processing rules comparable to Expert System Shells.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Addressing the global challenge of breast cancer, this research explores the fusion of generative AI, focusing on ChatGPT 3.5 turbo model, and the intricacies of breast cancer risk assessment. The research aims to evaluate ChatGPT's reasoning capabilities, emphasizing its potential to process rules and provide explanations for screening recommendations. The study seeks to bridge the technology gap between intelligent machines and clinicians by demonstrating ChatGPT's unique proficiency in natural language reasoning. The methodology employs a supervised prompt-engineering approach to enforce detailed explanations for ChatGPT's recommendations. Synthetic use cases, generated algorithmically, serve as the testing ground for the encoded rules, evaluating the model's processing prowess. Findings highlight ChatGPT's promising capacity in processing rules comparable to Expert System Shells, with a focus on natural language reasoning. The research introduces the concept of reinforcement explainability, showcasing its potential in elucidating outcomes and facilitating user-friendly interfaces for breast cancer risk assessment.

Related papers

• Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique [66.94905631175209]
  We propose a novel inference-time scaling approach -- stepwise natural language self-critique (PANEL) It employs self-generated natural language critiques as feedback to guide the step-level search process. This approach bypasses the need for task-specific verifiers and the associated training overhead.
  arXiv  Detail & Related papers  (2025-03-21T17:59:55Z)
• Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
  We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
  arXiv  Detail & Related papers  (2023-11-11T11:01:13Z)
• DEMASQ: Unmasking the ChatGPT Wordsmith [63.8746084667206]
  We propose an effective ChatGPT detector named DEMASQ, which accurately identifies ChatGPT-generated content. Our method addresses two critical factors: (i) the distinct biases in text composition observed in human- and machine-generated content and (ii) the alterations made by humans to evade previous detection methods.
  arXiv  Detail & Related papers  (2023-11-08T21:13:05Z)
• Ethical Aspects of ChatGPT in Software Engineering Research [4.0594888788503205]
  ChatGPT can improve Software Engineering (SE) research practices by offering efficient, accessible information analysis and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research.
  arXiv  Detail & Related papers  (2023-06-13T06:13:21Z)
• ChatGPT is a Remarkable Tool -- For Experts [9.46644539427004]
  We explore the potential of ChatGPT to enhance productivity, streamline problem-solving processes, and improve writing style. We highlight the potential risks associated with excessive reliance on ChatGPT in these fields. We outline areas and objectives where ChatGPT proves beneficial, applications where it should be used judiciously, and scenarios where its reliability may be limited.
  arXiv  Detail & Related papers  (2023-06-02T06:28:21Z)
• ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about [15.19126287569545]
  This research examines the responses generated by ChatGPT from different Conversational QA corpora. The study employed BERT similarity scores to compare these responses with correct answers and obtain Natural Language Inference(NLI) labels. The study identified instances where ChatGPT provided incorrect answers to questions, providing insights into areas where the model may be prone to error.
  arXiv  Detail & Related papers  (2023-04-06T18:42:47Z)
• To ChatGPT, or not to ChatGPT: That is the question! [78.407861566006]
  This study provides a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. We have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
  arXiv  Detail & Related papers  (2023-04-04T03:04:28Z)
• Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms [0.8339831319589133]
  ChatGPT, developed by OpenAI, is a recent addition to the family of language models. We evaluate the performance of ChatGPT on Abstractive Summarization by the means of automated metrics and blinded human reviewers.
  arXiv  Detail & Related papers  (2023-03-30T18:28:33Z)
• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective [67.98821225810204]
  We evaluate the robustness of ChatGPT from the adversarial and out-of-distribution perspective. Results show consistent advantages on most adversarial and OOD classification and translation tasks. ChatGPT shows astounding performance in understanding dialogue-related texts.
  arXiv  Detail & Related papers  (2023-02-22T11:01:20Z)
• Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT [103.57103957631067]
  ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. We evaluate ChatGPT's understanding ability by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question answering tasks.
  arXiv  Detail & Related papers  (2023-02-19T12:29:33Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)

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.