Neural Topic Modeling with Large Language Models in the Loop

• URL: http://arxiv.org/abs/2411.08534v1
• Date: Wed, 13 Nov 2024 11:31:02 GMT
• Title: Neural Topic Modeling with Large Language Models in the Loop
• Authors: Xiaohao Yang, He Zhao, Weijie Xu, Yuanyuan Qi, Jueqing Lu, Dinh Phung, Lan Du,
• Abstract summary: We propose a novel framework that integrates Large Language Models (LLMs) with Neural Topic Models (NTMs) In LLM-ITL, global topics and document representations are learned through the NTM, while an LLM refines the topics via a confidence-weighted Optimal Transport (OT)-based alignment objective. This process enhances the interpretability and coherence of the learned topics, while maintaining the efficiency of NTMs.
• Score: 12.142323482188056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. To address these limitations, we propose LLM-ITL, a novel LLM-in-the-loop framework that integrates LLMs with many existing Neural Topic Models (NTMs). In LLM-ITL, global topics and document representations are learned through the NTM, while an LLM refines the topics via a confidence-weighted Optimal Transport (OT)-based alignment objective. This process enhances the interpretability and coherence of the learned topics, while maintaining the efficiency of NTMs. Extensive experiments demonstrate that LLM-ITL can help NTMs significantly improve their topic interpretability while maintaining the quality of document representation.

Related papers

• Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model [63.14883657299359]
  Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. tuning MLLMs for downstream tasks encounters two key challenges: Task-Expert, where distribution shifts between pre-training and target datasets constrain target performance, and OpenWorld Stabilization, where catastrophic forgetting erases the model general knowledge.
  arXiv  Detail & Related papers  (2025-03-06T15:29:13Z)
• LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework [0.0]
  Large language models (LLMs) offer potential for dynamic topic refinement and discovery, yet their application often incurs high API costs. To address these challenges, we propose the LLM-assisted Iterative Topic Augmentation framework (LITA) LITA integrates user-provided seeds with embedding-based clustering and iterative refinement.
  arXiv  Detail & Related papers  (2024-12-17T01:43:44Z)
• All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks [51.19110891434727]
  Large Language Models (LLMs) with pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. E-LLaGNN is a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph.
  arXiv  Detail & Related papers  (2024-07-20T22:09:42Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling [1.0345450222523374]
  Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling. This paper focuses on addressing the issues of topic granularity and hallucinations for better LLM-based topic modelling. Our approach does not rely on traditional human annotation to rank preferred answers but employs a reconstruction pipeline to modify raw topics.
  arXiv  Detail & Related papers  (2024-05-01T16:32:07Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling [0.9095496510579351]
  We investigate the untapped potential of large language models (LLMs) as an alternative for uncovering the underlying topics within extensive text corpora. Our findings indicate that LLMs with appropriate prompts can stand out as a viable alternative, capable of generating relevant topic titles and adhering to human guidelines to refine and merge topics.
  arXiv  Detail & Related papers  (2024-03-24T17:39:51Z)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models [4.873927154453253]
  Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety of downstream natural language processing tasks. Simul-LLM is the first open-source fine-tuning and evaluation pipeline development framework for LLMs focused on SimulMT.
  arXiv  Detail & Related papers  (2023-12-07T20:42:05Z)
• Exploring the Potential of Large Language Models in Computational Argumentation [54.85665903448207]
  Large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-11-15T15:12:15Z)
• Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models [27.841725567976315]
  Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), but their lack of interpretability has been a major concern. In this work, we introduce proto-lm, a prototypical network-based white-box framework that allows LLMs to learn immediately interpretable embeddings. Our method's applicability and interpretability are demonstrated through experiments on a wide range of NLP tasks, and our results indicate a new possibility of creating interpretable models without sacrificing performance.
  arXiv  Detail & Related papers  (2023-11-03T05:55:32Z)
• DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM [2.8233611508673]
  Our study addresses gaps by introducing a novel framework named Diffusion-Enhanced Topic Modeling. By exploiting the power of diffusion model, our framework also provides the capability to do topic based text generation.
  arXiv  Detail & Related papers  (2023-10-23T19:03:04Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning [104.58874584354787]
  In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
  arXiv  Detail & Related papers  (2023-01-27T18:59:01Z)
• Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations [35.74225306947918]
  We propose a joint latent space learning and clustering framework built upon PLM embeddings. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery.
  arXiv  Detail & Related papers  (2022-02-09T17:26:08Z)

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.