LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
- URL: http://arxiv.org/abs/2406.09008v1
- Date: Thu, 13 Jun 2024 11:19:50 GMT
- Title: LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
- Authors: Xiaohao Yang, He Zhao, Dinh Phung, Wray Buntine, Lan Du,
- Abstract summary: We propose WALM (Words Agreement with Language Model), a new evaluation method for topic modeling.
With extensive experiments involving different types of topic models, WALM is shown to align with human judgment.
- Score: 12.500091504010067
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Topic modeling has been a widely used tool for unsupervised text analysis. However, comprehensive evaluations of a topic model remain challenging. Existing evaluation methods are either less comparable across different models (e.g., perplexity) or focus on only one specific aspect of a model (e.g., topic quality or document representation quality) at a time, which is insufficient to reflect the overall model performance. In this paper, we propose WALM (Words Agreement with Language Model), a new evaluation method for topic modeling that comprehensively considers the semantic quality of document representations and topics in a joint manner, leveraging the power of large language models (LLMs). With extensive experiments involving different types of topic models, WALM is shown to align with human judgment and can serve as a complementary evaluation method to the existing ones, bringing a new perspective to topic modeling. Our software package will be available at https://github.com/Xiaohao-Yang/Topic_Model_Evaluation, which can be integrated with many widely used topic models.
Related papers
- VHELM: A Holistic Evaluation of Vision Language Models [75.88987277686914]
We present the Holistic Evaluation of Vision Language Models (VHELM)
VHELM aggregates various datasets to cover one or more of the 9 aspects: visual perception, knowledge, reasoning, bias, fairness, multilinguality, robustness, toxicity, and safety.
Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast.
arXiv Detail & Related papers (2024-10-09T17:46:34Z) - Investigating the Impact of Text Summarization on Topic Modeling [13.581341206178525]
In this paper, an approach is proposed that further enhances topic modeling performance by utilizing a pre-trained large language model (LLM)
Few shot prompting is used to generate summaries of different lengths to compare their impact on topic modeling.
The proposed method yields better topic diversity and comparable coherence values compared to previous models.
arXiv Detail & Related papers (2024-09-28T19:45:45Z) - Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities [89.40778301238642]
Model merging is an efficient empowerment technique in the machine learning community.
There is a significant gap in the literature regarding a systematic and thorough review of these techniques.
arXiv Detail & Related papers (2024-08-14T16:58:48Z) - Interactive Topic Models with Optimal Transport [75.26555710661908]
We present EdTM, as an approach for label name supervised topic modeling.
EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
arXiv Detail & Related papers (2024-06-28T13:57:27Z) - OLMES: A Standard for Language Model Evaluations [64.85905119836818]
We propose OLMES, a practical, open standard for reproducible language model evaluations.
We identify and review the varying factors in evaluation practices adopted by the community.
OLMES supports meaningful comparisons between smaller base models that require the unnatural "cloze" formulation of multiple-choice questions.
arXiv Detail & Related papers (2024-06-12T17:37:09Z) - GINopic: Topic Modeling with Graph Isomorphism Network [0.8962460460173959]
We introduce GINopic, a topic modeling framework based on graph isomorphism networks to capture the correlation between words.
We demonstrate the effectiveness of GINopic compared to existing topic models and highlight its potential for advancing topic modeling.
arXiv Detail & Related papers (2024-04-02T17:18:48Z) - Improving the TENOR of Labeling: Re-evaluating Topic Models for Content
Analysis [5.757610495733924]
We conduct the first evaluation of neural, supervised and classical topic models in an interactive task based setting.
We show that current automated metrics do not provide a complete picture of topic modeling capabilities.
arXiv Detail & Related papers (2024-01-29T17:54:04Z) - Let the Pretrained Language Models "Imagine" for Short Texts Topic
Modeling [29.87929724277381]
In short texts, co-occurrence information is minimal, which results in feature sparsity in document representation.
Existing topic models (probabilistic or neural) mostly fail to mine patterns from them to generate coherent topics.
We extend short text into longer sequences using existing pre-trained language models (PLMs)
arXiv Detail & Related papers (2023-10-24T00:23:30Z) - EvalCrafter: Benchmarking and Evaluating Large Video Generation Models [70.19437817951673]
We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities.
Our approach involves generating a diverse and comprehensive list of 700 prompts for text-to-video generation.
Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmark, in terms of visual qualities, content qualities, motion qualities, and text-video alignment with 17 well-selected objective metrics.
arXiv Detail & Related papers (2023-10-17T17:50:46Z) - Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost.
Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
arXiv Detail & Related papers (2023-03-13T16:00:31Z) - 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.