LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework

• URL: http://arxiv.org/abs/2412.12459v1
• Date: Tue, 17 Dec 2024 01:43:44 GMT
• Title: LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework
• Authors: Chia-Hsuan Chang, Jui-Tse Tsai, Yi-Hang Tsai, San-Yih Hwang,
• Abstract summary: Large language models (LLMs) offer potential for dynamic topic refinement and discovery, yet their application often incurs high API costs.<n>To address these challenges, we propose the LLM-assisted Iterative Topic Augmentation framework (LITA)<n>LITA integrates user-provided seeds with embedding-based clustering and iterative refinement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topic modeling is widely used for uncovering thematic structures within text corpora, yet traditional models often struggle with specificity and coherence in domain-focused applications. Guided approaches, such as SeededLDA and CorEx, incorporate user-provided seed words to improve relevance but remain labor-intensive and static. 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), an LLM-assisted approach that integrates user-provided seeds with embedding-based clustering and iterative refinement. LITA identifies a small number of ambiguous documents and employs an LLM to reassign them to existing or new topics, minimizing API costs while enhancing topic quality. Experiments on two datasets across topic quality and clustering performance metrics demonstrate that LITA outperforms five baseline models, including LDA, SeededLDA, CorEx, BERTopic, and PromptTopic. Our work offers an efficient and adaptable framework for advancing topic modeling and text clustering.

Related papers

• From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System [49.57258257916805]
  Large Language Models (LLMs) demonstrate strong zero-shot recommendation capabilities. Practical applications often favor smaller, internally managed recommender models due to scalability, interpretability, and data privacy constraints. We propose an active data augmentation framework that synthesizes conversational training data by leveraging black-box LLMs guided by active learning techniques.
  arXiv  Detail & Related papers  (2025-04-21T23:05:47Z)
• Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework [81.29965270493238]
  We develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. We introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data.
  arXiv  Detail & Related papers  (2025-01-16T16:19:53Z)
• LLMs are Also Effective Embedding Models: An In-depth Overview [40.53941563464671]
  Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks.<n>Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs like GPT, LLaMA, and Mistral.
  arXiv  Detail & Related papers  (2024-12-17T06:48:24Z)
• Neural Topic Modeling with Large Language Models in the Loop [12.142323482188056]
  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.
  arXiv  Detail & Related papers  (2024-11-13T11:31:02Z)
• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Making Text Embedders Few-Shot Learners [33.50993377494602]
  We introduce a novel model bge-en-icl, which employs few-shot examples to produce high-quality text embeddings. Our approach integrates task-related examples directly into the query side, resulting in significant improvements across various tasks. Experimental results on the MTEB and AIR-Bench benchmarks demonstrate that our approach sets new state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2024-09-24T03:30:19Z)
• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning [72.90823351726374]
  We introduce the Unified framework for Large Language Model Embedding (ULLME), a flexible, plug-and-play implementation that enables bidirectional attention across various LLMs. We also propose Generation-augmented Representation Learning (GRL), a novel fine-tuning method to boost LLMs for text embedding tasks. To showcase our framework's flexibility and effectiveness, we release three pre-trained models from ULLME with different backbone architectures.
  arXiv  Detail & Related papers  (2024-08-06T18:53:54Z)
• 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)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• 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)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)

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.