インサイト - Natural Language Processing - # LLM-based Topic Modelling with Granularity Control and Hallucination Mitigation

Improving Topic Granularity and Mitigating Hallucinations in Large Language Models for Topic Modelling

Q: How can the potential limitations of the DPO fine-tuning framework be further improved?

While the Direct Preference Optimization (DPO) fine-tuning framework offers a promising approach for enhancing Large Language Models (LLMs), there are potential limitations that can be further improved. Some ways to address these limitations include: Sample Efficiency: One limitation of DPO is the need for a large number of training samples to effectively optimize the model. To improve sample efficiency, techniques such as data augmentation, transfer learning, or semi-supervised learning can be employed to make the most of limited training data. Generalization: DPO fine-tuning may lead to overfitting on the training data, limiting the model's ability to generalize to unseen examples. Regularization techniques, such as dropout or weight decay, can be applied to prevent overfitting and improve the model's generalization capabilities. Hyperparameter Tuning: The performance of the DPO framework is sensitive to hyperparameters such as the reward scaling factor (β). Conducting thorough hyperparameter tuning using techniques like grid search or Bayesian optimization can help optimize the model's performance. Robustness to Adversarial Inputs: LLMs fine-tuned with DPO may still be susceptible to adversarial inputs that can lead to unexpected behavior. Adversarial training methods, such as adversarial data augmentation or adversarial training objectives, can be employed to enhance the model's robustness against adversarial attacks. Interpretability: Improving the interpretability of the DPO fine-tuned models is essential for understanding the decision-making process. Techniques like attention visualization, saliency maps, or model-agnostic interpretability methods can be utilized to provide insights into the model's inner workings. By addressing these potential limitations through advanced techniques and methodologies, the DPO fine-tuning framework can be further improved to enhance the performance and robustness of LLM-based applications.

Q: Given the importance of topic modelling in various domains, how can the insights from this work be applied to enhance decision-making and knowledge discovery in fields such as healthcare or policy analysis?

The insights from this work on addressing topic granularity and hallucination in Large Language Models (LLMs) can be applied to enhance decision-making and knowledge discovery in fields such as healthcare or policy analysis in the following ways: Healthcare Decision-Making: Clinical Text Analysis: By fine-tuning LLMs with DPO to extract relevant and coherent topics from medical records, healthcare providers can gain valuable insights for diagnosis, treatment planning, and patient care. Drug Discovery: Applying the DPO framework to LLMs for topic modelling in pharmaceutical research can help identify patterns in drug interactions, side effects, and efficacy, leading to informed decision-making in drug development. Policy Analysis: Legislative Text Mining: Utilizing fine-tuned LLMs to analyze policy documents and legislative texts can aid policymakers in understanding trends, public sentiment, and potential gaps in policy implementation. Public Opinion Analysis: By applying the insights from topic modelling to analyze public discourse and sentiment on social media or news articles, policymakers can make data-driven decisions and formulate policies that align with public preferences. Knowledge Discovery: Research Literature Analysis: Fine-tuning LLMs with DPO for topic modelling on research articles can facilitate knowledge discovery by identifying emerging trends, gaps in research, and interdisciplinary connections in scientific literature. Trend Forecasting: Leveraging LLMs to analyze topics in social media discussions, news articles, or market reports can help predict future trends, enabling proactive decision-making in various domains. By integrating the insights from this work into healthcare, policy analysis, and knowledge discovery processes, stakeholders can leverage advanced natural language processing techniques to extract actionable insights, improve decision-making, and drive innovation in diverse fields.

核心概念

This paper introduces a novel approach to fine-tune open-source large language models (LLMs) to address the issues of topic granularity and hallucination in LLM-based topic modelling.

要約

The paper focuses on addressing the challenges of topic granularity and hallucinations in LLM-based topic modelling. The key highlights are:

Experiments reveal that off-the-shelf LLMs tend to generate a large number of near-duplicate topics with inconsistent naming, even when provided with prompts specifying topic granularity requirements.
The authors propose a fine-tuning pipeline using Direct Preference Optimization (DPO) to enhance LLM performance on topic modelling. This approach does not require human annotations, but instead leverages a reconstruction pipeline to modify raw topics generated by LLMs.
Comparative experiments show that the fine-tuned TopicMistral model significantly outperforms off-the-shelf LLMs in generating more coherent, relevant, and precise topics. It also reduces the number of hallucinated topics.
The paper introduces novel evaluation protocols to assess the quality of topics extracted by LLMs, including metrics for topic naming adherence, human expectation alignment, and hallucination risk.
Further analysis reveals that TopicMistral aggregates less hallucinated topic information during the attention propagation within the deep layers, contributing to its improved performance.

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統計

LLMs tend to generate a large number of near-duplicate topics with inconsistent naming.
TopicMistral, the fine-tuned model, generates 90% fewer unique topics compared to off-the-shelf LLMs on the Bills dataset.
TopicMistral achieves a low average hallucination rate of 1% versus 7% for off-the-shelf Mistral.
TopicMistral has a higher adherence rate of 97% versus 16% for off-the-shelf Mistral when provided with adversarial prompts.

引用

"To the best of our knowledge, this is the first paper to explore the risks of hallucination across various API-based and open source LLMs within the context of LLM-driven topic modelling."
"Experimental results demonstrate that our fine-tuned LLM (TopicMistral) significantly reduces unlreated topics and hallucinated topics observed in off-the-shelf LLM outputs."
"Our fine-tuning approach has significantly reduced the number of hallucinated topics across different adversarial prompt settings."

抽出されたキーインサイト

Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling

by Yida Mu,Peiz... 場所 arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00611.pdf

Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling

深掘り質問

How can the proposed fine-tuning approach be extended to other LLM-based applications beyond topic modelling?

The proposed fine-tuning approach using Direct Preference Optimization (DPO) can be extended to various other Large Language Model (LLM) applications beyond topic modelling by adapting the methodology to suit the specific requirements of different tasks. Here are some ways in which the approach can be extended:

Text Generation: The DPO fine-tuning framework can be applied to enhance text generation tasks such as story writing, dialogue generation, or content creation. By providing feedback on the preferred outputs, the LLM can be fine-tuned to generate more coherent and contextually relevant text.

Sentiment Analysis: For sentiment analysis tasks, the DPO framework can be used to fine-tune LLMs to better understand and classify emotions in text. By optimizing for direct preferences in sentiment classification, the model can be improved to accurately identify sentiments in user-generated content.

Question Answering: In question answering applications, the DPO approach can be utilized to train LLMs to provide more accurate and relevant answers to user queries. By optimizing for direct preferences in generating responses, the model can be fine-tuned to enhance its performance in answering a wide range of questions.

Language Translation: For language translation tasks, the DPO fine-tuning framework can be employed to improve the accuracy and fluency of translated text. By optimizing for direct preferences in generating translated content, LLMs can be fine-tuned to produce more natural and contextually appropriate translations.

Summarization: In text summarization applications, the DPO approach can be used to fine-tune LLMs to generate concise and informative summaries of longer texts. By optimizing for direct preferences in summarizing content, the model can be enhanced to produce more coherent and relevant summaries.

By adapting the DPO fine-tuning framework to these and other LLM-based applications, it is possible to improve the performance and effectiveness of various natural language processing tasks beyond topic modelling.

How can the potential limitations of the DPO fine-tuning framework be further improved?

While the Direct Preference Optimization (DPO) fine-tuning framework offers a promising approach for enhancing Large Language Models (LLMs), there are potential limitations that can be further improved. Some ways to address these limitations include:

Sample Efficiency: One limitation of DPO is the need for a large number of training samples to effectively optimize the model. To improve sample efficiency, techniques such as data augmentation, transfer learning, or semi-supervised learning can be employed to make the most of limited training data.

Generalization: DPO fine-tuning may lead to overfitting on the training data, limiting the model's ability to generalize to unseen examples. Regularization techniques, such as dropout or weight decay, can be applied to prevent overfitting and improve the model's generalization capabilities.

Hyperparameter Tuning: The performance of the DPO framework is sensitive to hyperparameters such as the reward scaling factor (β). Conducting thorough hyperparameter tuning using techniques like grid search or Bayesian optimization can help optimize the model's performance.

Robustness to Adversarial Inputs: LLMs fine-tuned with DPO may still be susceptible to adversarial inputs that can lead to unexpected behavior. Adversarial training methods, such as adversarial data augmentation or adversarial training objectives, can be employed to enhance the model's robustness against adversarial attacks.

Interpretability: Improving the interpretability of the DPO fine-tuned models is essential for understanding the decision-making process. Techniques like attention visualization, saliency maps, or model-agnostic interpretability methods can be utilized to provide insights into the model's inner workings.

By addressing these potential limitations through advanced techniques and methodologies, the DPO fine-tuning framework can be further improved to enhance the performance and robustness of LLM-based applications.

Given the importance of topic modelling in various domains, how can the insights from this work be applied to enhance decision-making and knowledge discovery in fields such as healthcare or policy analysis?

The insights from this work on addressing topic granularity and hallucination in Large Language Models (LLMs) can be applied to enhance decision-making and knowledge discovery in fields such as healthcare or policy analysis in the following ways:

Healthcare Decision-Making:

Clinical Text Analysis: By fine-tuning LLMs with DPO to extract relevant and coherent topics from medical records, healthcare providers can gain valuable insights for diagnosis, treatment planning, and patient care.
Drug Discovery: Applying the DPO framework to LLMs for topic modelling in pharmaceutical research can help identify patterns in drug interactions, side effects, and efficacy, leading to informed decision-making in drug development.

Policy Analysis:

Legislative Text Mining: Utilizing fine-tuned LLMs to analyze policy documents and legislative texts can aid policymakers in understanding trends, public sentiment, and potential gaps in policy implementation.
Public Opinion Analysis: By applying the insights from topic modelling to analyze public discourse and sentiment on social media or news articles, policymakers can make data-driven decisions and formulate policies that align with public preferences.

Knowledge Discovery:

Research Literature Analysis: Fine-tuning LLMs with DPO for topic modelling on research articles can facilitate knowledge discovery by identifying emerging trends, gaps in research, and interdisciplinary connections in scientific literature.
Trend Forecasting: Leveraging LLMs to analyze topics in social media discussions, news articles, or market reports can help predict future trends, enabling proactive decision-making in various domains.

By integrating the insights from this work into healthcare, policy analysis, and knowledge discovery processes, stakeholders can leverage advanced natural language processing techniques to extract actionable insights, improve decision-making, and drive innovation in diverse fields.