On Fusing ChatGPT and Ensemble Learning in Discon-tinuous Named Entity Recognition in Health Corpora (2412.16976v1)

Abstract: Named Entity Recognition has traditionally been a key task in natural language processing, aiming to identify and extract important terms from unstructured text data. However, a notable challenge for contemporary deep-learning NER models has been identifying discontinuous entities, which are often fragmented within the text. To date, methods to address Discontinuous Named Entity Recognition have not been explored using ensemble learning to the best of our knowledge. Furthermore, the rise of LLMs, such as ChatGPT in recent years, has shown significant effectiveness across many NLP tasks. Most existing approaches, however, have primarily utilized ChatGPT as a problem-solving tool rather than exploring its potential as an integrative element within ensemble learning algorithms. In this study, we investigated the integration of ChatGPT as an arbitrator within an ensemble method, aiming to enhance performance on DNER tasks. Our method combines five state-of-the-art NER models with ChatGPT using custom prompt engineering to assess the robustness and generalization capabilities of the ensemble algorithm. We conducted experiments on three benchmark medical datasets, comparing our method against the five SOTA models, individual applications of GPT-3.5 and GPT-4, and a voting ensemble method. The results indicate that our proposed fusion of ChatGPT with the ensemble learning algorithm outperforms the SOTA results in the CADEC, ShARe13, and ShARe14 datasets, showcasing its potential to enhance NLP applications in the healthcare domain.

• The paper presents a novel ensemble learning framework integrating ChatGPT to enhance Discontinuous Named Entity Recognition (DNER) in medical texts.
• Evaluating across three health datasets, the proposed ensemble model significantly outperforms baseline methods and standalone LLMs, improving F1 scores by up to 1.13%.
• This research demonstrates a new paradigm for using generative AI in ensemble systems, offering practical applications for improving information extraction from complex healthcare data.

Integration of ChatGPT and Ensemble Learning in Discontinuous Named Entity Recognition within Health Corpora

The paper "On Fusing ChatGPT and Ensemble Learning in Discontinuous Named Entity Recognition in Health Corpora" presents an innovative methodology for enhancing Discontinuous Named Entity Recognition (DNER) in medical texts by integrating ChatGPT within an ensemble learning framework. Recognizing discontinuous entities in text represents a complex challenge in NLP, particularly pertinent in health domain datasets where entities can frequently appear non-contiguously. This research addresses this challenge through an ensemble approach that strategically leverages the generative capabilities of advanced LLMs such as ChatGPT.

Methodology and Approach

The authors propose an ensemble method that amalgamates five state-of-the-art NER models with ChatGPT, using customized prompt engineering to refine and direct the LLM's predictions. This method positions ChatGPT as an arbitrator among the ensemble models, theoretically enhancing generalization and robustness in the context of DNER tasks. The experimental setup involves evaluations across three benchmark medical datasets: CADEC, ShARe13, and ShARe14, comparing the proposed ensemble method against individual SOTA models, standalone instances of GPT-3.5 and GPT-4, as well as a traditional voting ensemble method.

Numerical Results and Implications

Quantitative assessments reveal that the fusion strategy outperforms individual baseline models and conventional voting ensemble methods, demonstrating improvements in F1-scores by approximately 1.13%, 0.54%, and 0.67% on the CADEC, ShARe13, and ShARe14 datasets, respectively. When compared to the outputs from GPT-3.5 and GPT-4, the proposed ensemble achieves approximately 7.42%, 0.89%, and 0.54% higher average results, underscoring the efficacy of combining ensemble learning with LLM-driven prompting. The paper achieves this improvement by leveraging the inherent adaptability of ChatGPT through prompt engineering to mitigate issues such as synonym substitution and input ordering, both of which can adversely affect model consistency.

Theoretical and Practical Implications

This research sets a precedent for utilizing generative AI as an active component in ensemble systems, contrasting traditional approaches where LLMs like ChatGPT have predominantly been used in isolation. The paper enhances the theoretical understanding of ensemble learning by introducing a novel integration paradigm, where a generative model's arbitrary decisions aid in consolidating predictions from disparate NER models. Practically, the potential application of this method in the healthcare sector can improve information extraction from complex datasets like electronic health records, aiding clinical decision-making and research efforts involving biomedical texts.

Future Directions

Future research may focus on refining the prompt engineering techniques employed, further controlling the generative model’s propensity for hallucinations, an attribute that contributes to high recall but can occasionally diminish precision due to false-positive entities. Exploration into adaptive prompt strategies that dynamically tailor themselves based on model feedback could enhance effectiveness. Moreover, extending the ensemble framework to integrate additional contextual data or newer iterations of LLMs presents an avenue for subsequent investigation.

In conclusion, the paper provides a robust framework for enhancing DNER through the strategic coupling of ChatGPT within an ensemble framework, delineating a pathway for advancing NLP applications in the healthcare domain.