Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis

• URL: http://arxiv.org/abs/2402.14484v2
• Date: Fri, 23 Feb 2024 11:50:18 GMT
• Title: Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis
• Authors: Takehiro Takayanagi and Masahiro Suzuki and Ryotaro Kobayashi and Hiroki Sakaji and Kiyoshi Izumi
• Abstract summary: This study conducts comprehensive evaluations of ChatGPT's causal text mining capabilities. We introduce a benchmark that extends beyond general English datasets. We also provide an evaluation framework to ensure fair comparisons between ChatGPT and previous approaches.
• Score: 8.031131164056347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causality is fundamental in human cognition and has drawn attention in diverse research fields. With growing volumes of textual data, discerning causalities within text data is crucial, and causal text mining plays a pivotal role in extracting meaningful patterns. This study conducts comprehensive evaluations of ChatGPT's causal text mining capabilities. Firstly, we introduce a benchmark that extends beyond general English datasets, including domain-specific and non-English datasets. We also provide an evaluation framework to ensure fair comparisons between ChatGPT and previous approaches. Finally, our analysis outlines the limitations and future challenges in employing ChatGPT for causal text mining. Specifically, our analysis reveals that ChatGPT serves as a good starting point for various datasets. However, when equipped with a sufficient amount of training data, previous models still surpass ChatGPT's performance. Additionally, ChatGPT suffers from the tendency to falsely recognize non-causal sequences as causal sequences. These issues become even more pronounced with advanced versions of the model, such as GPT-4. In addition, we highlight the constraints of ChatGPT in handling complex causality types, including both intra/inter-sentential and implicit causality. The model also faces challenges with effectively leveraging in-context learning and domain adaptation. We release our code to support further research and development in this field.

Related papers

• Exploring the Capability of ChatGPT to Reproduce Human Labels for Social Computing Tasks (Extended Version) [26.643834593780007]
  We investigate the extent to which ChatGPT can annotate data for social computing tasks. ChatGPT exhibits promise in handling data annotation tasks, albeit with some challenges. We propose GPT-Rater, a tool to predict if ChatGPT can correctly label data for a given annotation task.
  arXiv  Detail & Related papers  (2024-07-08T22:04:30Z)
• 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)
• Chatbots Are Not Reliable Text Annotators [0.0]
  ChatGPT is a closed-source product which has major drawbacks with regards to transparency, cost, and data protection. Recent advances in open-source (OS) large language models (LLMs) offer alternatives which remedy these challenges.
  arXiv  Detail & Related papers  (2023-11-09T22:28:14Z)
• Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors [14.911130381374793]
  We first evaluate ChatGPT's performance on 17 datasets with 14 IE sub-tasks under the zero-shot, few-shot and chain-of-thought scenarios. We then analyze the robustness of ChatGPT on 14 IE sub-tasks, and find that: 1) ChatGPT rarely outputs invalid responses; 2) Irrelevant context and long-tail target types greatly affect ChatGPT's performance; and 3) ChatGPT cannot understand well the subject-object relationships in RE task.
  arXiv  Detail & Related papers  (2023-05-23T18:17:43Z)
• CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts [10.034193809833372]
  Malicious users could synthesize dummy academic content through ChatGPT. We present a large-scale CHatGPT-writtEn AbsTract dataset (CHEAT) to support the development of detection algorithms.
  arXiv  Detail & Related papers  (2023-04-24T11:19:33Z)
• 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)
• Is ChatGPT a Good NLG Evaluator? A Preliminary Study [121.77986688862302]
  We provide a preliminary meta-evaluation on ChatGPT to show its reliability as an NLG metric. Experimental results show that compared with previous automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation with human judgments. We hope our preliminary study could prompt the emergence of a general-purposed reliable NLG metric.
  arXiv  Detail & Related papers  (2023-03-07T16:57:20Z)
• 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)
• Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [113.22611481694825]
  Large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community. It is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot.
  arXiv  Detail & Related papers  (2023-02-08T09:44:51Z)
• A Categorical Archive of ChatGPT Failures [47.64219291655723]
  ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study.
  arXiv  Detail & Related papers  (2023-02-06T04:21:59Z)

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.