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DSL-Topic: Improving Topic Modeling by Distilling Soft Labelsfrom Language Models
arXiv:2602.17907v2 Announce Type: replace Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity. In this work, we introduce a novel topic model training framework by Distilling Soft Labels (DSL) from Language Models (LMs). To construct the contextually enriched reconstruction signals, we project the next token probabilities, conditioned on a...
Changing topic bias in biomedical science maps by linking documents through alternative data sources: policy documents, patents, authors, Facebook, and Twitter
arXiv:2412.07550v4 Announce Type: replace Abstract: Traditional science maps visualize topics by clustering documents within a network, but they are inherently biased toward clustering certain topics over others. If these topics could be chosen, then the science maps could be tailored for different needs. In this paper, we explore the extent to which the topic bias of a science map can be changed by choosing different data sources to build the document network.
LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models
Announce Type: replace Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. Recent LLM-based refinements improve interpretability but are costly, document-level, and prone to hallucination, with prior white-box approaches requiring inaccessible token probabilities. We propose LLM-XTM, a framework that integrates LLM-guided topic...
Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study
Announce Type: new Abstract: Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. We present a zero-shot multi-label topic classification framework and systematically investigate how per-article knowledge graph augmentation affects its performance. The base framework classifies topics in documents without labeled training data and has four variants: article-only classification, keyword-enhanced...
Disentangling Similarity and Relatedness in Topic Models
Announce Type: replace Abstract: The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic models differ from classical co-occurrence models such as Latent Dirichlet Allocation (LDA) not only in performance, but also in the type of semantic structure they capture. We formalize this distinction along two psycholinguistic axes: thematic relatedness (dog/bone) and taxonomic similarity (dog/wolf).
Emerging and established topics in drone research: Citation impact and knowledge flows across China, the United States, the EU, Ukraine, and Russia (2020-2025)
arXiv:2606.03362v1 Announce Type: new Abstract: This study examined emerging and established topics in drone research, focusing on citation impact and knowledge flows across China, the United States, the EU, Ukraine, and Russia between 2020 and 2025 using OpenAlex bibliographic data. The findings revealed that drone-related science is characterised by growing geopolitical asymmetries in scientific production, citation concentration, and international knowledge exchange.
Anthropic says these topics are too dangerous to let its Fable 5 model talk about
Anthropic Tuesday publicly released Claude Fable 5, its first "Mythos-class" model that it says surpasses its previous frontier Opus models in overall capabilities. But the model's launch today comes with safeguards designed to prevent it from answering queries on topics like cybersecurity, biology, and chemistry, where the company has publicly worried about its potential impact to "uplift" malicious actors. Anthropic says Fable 5 operates on the "same underlying model" as Mythos 5, which is...
CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation
arXiv:2605.30668v1 Announce Type: new Abstract: Dialogue topic segmentation is critical in many human-AI collaborative applications which requires identifying heterogeneous boundary cues, including lexical transitions near utterance edges and semantic discontinuities across utterances. Existing utterance models often dilute these local lexical signals. We propose CobSeg, a novel multi-branch architecture that separates coherence-level semantic continuity from lexical boundary transitions and...
Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
Announce Type: new Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label. Using articles from AllSides, paired with shared sentiment annotations from Llama-3.3-70b-versatile, we compare ideology labels from expert human annotators, GPT-4o-mini (baseline and finetuned), and Llama-3.3-70B. We apply Double Machine Learning (DML) and community-level mediation analysis across all four...
A Double Bind: Gendered Funding, Research Topics, and Academic Performance in the Social Sciences
arXiv:2606.03742v2 Announce Type: replace Abstract: While female representation in social sciences is increasing, systemic gender disparities may persist in research funding and academic performance. Some argue that female scholars now receive equal opportunities, yet evidence suggests that gender imbalances remain, particularly in specific research areas. This study examines 12,945 National Science Foundation (NSF)-funded principal investigators in social sciences from 2000 to 2019 to...