Enhancing Short-Text Topic Modeling with LLM -Driven Context Expansion and Prefix-Tuned VAE s (2024)

Abstract: Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking in short texts. Therefore, existing approaches, whether probabilistic or neural, frequently struggle to extract meaningful patterns from such data, resulting in incoherent topics. To address this challenge, we propose a novel approach that leverages large language models (LLMs) to extend short texts into more detailed sequences before applying topic modeling. To further improve the efficiency and solve the problem of semantic inconsistency from LLM-generated texts, we propose to use prefix tuning to train a smaller language model coupled with a variational autoencoder for short-text topic modeling. Our method significantly improves short-text topic modeling performance, as demonstrated by extensive experiments on real-world datasets with extreme data sparsity, outperforming current state-of-the-art topic models.

## introduction in the digital era, short texts like tweets, web page titles, news headlines, image captions, and product reviews are prevalent for sharing knowledge. however, the sheer volume of these texts necessitates efficient information extraction mechanisms. topic modeling is a key method for uncovering latent topics in short texts, with applications including comment summarization @xcite , content characterization @xcite , emergent topic detection @xcite , document classification @xcite , user interest profiling @xcite , and so on. traditional topic models, such as lda and plsa, are designed to uncover latent topics given a corpus of documents by analyzing word cooccurrences within the texts @xcite . these models assume that each document contains enough text to provide meaningful co-occurrence information. however, in short texts like titles, captions, and headlines, this assumption does not hold due to the limited text in each document. this scarcity leads to a data sparsity problem, where limited word co-occurrences make it difficult for traditional models to effectively mine high-quality topics. the primary challenge is that each document is short, rather than the corpus being insufficient in size. while various strategies have been developed for modeling topics in short texts, each has its limitations. e.g., aggregating short texts into longer pseudo-documents based on metadata like user information, hashtags, or external corpora is a common approach @xcite ; @xcite ; @xcite ; however, the availability of such metadata can be inconsistent. to overcome this, some methods rely on structural or semantic information within the texts themselves, such as the biterm topic model @xcite and its extensions @xcite , which focus on word pairs but often cannot provide individual document topic distributions. another method @xcite limits texts to a single topic, simplifying the model but potentially overlooking texts that span multiple topics. considering the limitations mentioned above, in this paper, we first try to understand the characteristics of short texts and how humans process these texts when detecting topics. a short text, such as a title or caption, typically serves as a summarized version of a longer text, providing readers with essential hints about the full content. when judging the topics of short texts, humans often infer the broader context based on their background knowledge and the cues provided in the text. for example, given the headline: "no tsunami but fifa's this leads us to the question: can a model similarly infer the broader context to better understand the topics of a short text? recently, large language models (llms) such as gpt-3 @xcite , llama2 @xcite , and t5 @xcite have demonstrated remarkable capabilities as open-ended text generators, capable of producing surprisingly fluent text from a limited preceding context. for example, given the abovementioned news headline, llms can generate extended sequences (as shown in the third and fourth columns of considering these capabilities, we first explore the potential solution for short-text topic modeling: leveraging large language models (llms) to generate a longer text from each short text in a corpus before applying traditional topic modeling techniques. by expanding short texts into more detailed, context-rich narratives, llms can create a proxy for the detailed context that traditional topic modeling techniques often lack when dealing with short texts. in other words, it is a proxy of humanlike inference of the broader context surrounding a given short text before mining the topics, as shown in figure 1 . while leveraging llms to expand short texts offers a promising solution, this approach faces sev-eral challenges. one key challenge is maintaining semantic consistency: ensuring that the generated longer texts accurately reflect the original short texts without introducing irrelevant or inaccurate information is difficult, as llms are not always fine-tuned for specific tasks or domains. this can lead to semantic drift, distorting the topic modeling results. another challenge is the latency of llm calls: generating extended texts online can be slow, making real-time topic detection impractical, even if offline generation during training is feasible. to tackle these challenges, we aim to avoid directly using llm-generated longer texts as input. instead, we train a model to learn topics from short texts and reconstruct longer texts previously generated by an llm. this minimizes the effects of any shift in meaning in the generated texts. by decoding topics from short texts before generating longer texts, we align with one of the llm's inherent characteristics. as noted by @xcite , llms implicitly engage in topic modeling by navigating a latent conceptual space to generate text, with each token generation influenced by an underlying topic variable. however, directly inferring these latent concepts into discrete topics like latent dirichlet allocation (lda) is not straightforward. to bridge this gap, we introduce the prefix-tuned variational topic model (pvtm), which combines a smaller language model (lm) with a variational autoencoder (vae) for topic inference. instead of tuning the entire lm, we employ prefix tuning (li and liang, 2021), which fine-tunes only a small set of parameters, effectively capturing domainspecific features from short texts. this minimizes the effects of any semantic drift in the generated texts from larger, general llms. the extracted features from smaller lm serve as input for a vae to decode discrete topics. the features extracted from the smaller lm are then used by the vae to decode discrete topics. both the lm and vae are trained together with a topic modeling objective to ensure effective learning. the key insights of our solution include -(1) semantic consistency: by training on short texts and using generated longer texts only as output, we ensure the integrity of the original data and mitigate the risk of introducing irrelevant information. (2) efficiency: the reduced inference time of smaller lms and the efficiency of vaes in learning discrete topics make this method suitable for real-time topic detection applications. (3) prefix tuning: this finetuning method allows us to capture domain-specific features without the computational overhead of tuning large llms, ensuring scalability. to summarize, our contributions in this paper are the following. firstly, we explore llms for extending short texts into longer ones and then apply traditional topic models to the longer texts. secondly, to improve efficiency and address the issue of semantic drift, we propose a new framework consisting of a jointly trained smaller lm and vae. finally, we conduct a comprehensive set of experiments on multiple datasets across different tasks, demonstrating our model's superiority over existing baselines. ## proposed methodology our proposed framework consists of two components. the first component generates longer text given a short text. the second one utilizes the generated longer texts for topic modeling. ## experiments in this section, we employ empirical evaluations, which are designed mainly to fulfill the following objectives: ## conclusion in this paper, we address the issue of topic modeling for short texts. our approach focuses on improving the input representation of short texts and enhancing the model's ability to capture la-tent topics despite the limited contextual information. the input to our method consists of individual short texts, such as a collection of tweets or headlines, and the output is a set of coherent topics that summarize the main themes present in the corpus. by tackling the data sparsity problem, we aim to develop a more effective topic modeling framework for short texts. a set of empirical evaluations demonstrate the effectiveness of the proposed framework over the state-of-the-art.