Tootfinder

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang, Ecesu \"Urker, Tessa Verhoef, Gemma Boleda, Arianna Bisazza
https://arxiv.org/abs/2509.22479

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. En…

Analyzing Latent Concepts in Code Language Models
Arushi Sharma, Vedant Pungliya, Christopher J. Quinn, Ali Jannesari
https://arxiv.org/abs/2510.00476 https://

Analyzing Latent Concepts in Code Language Models
Interpreting the internal behavior of large language models trained on code remains a critical challenge, particularly for applications demanding trust, transparency, and semantic robustness. We propose Code Concept Analysis (CoCoA): a global post-hoc interpretability framework that uncovers emergent lexical, syntactic, and semantic structures in a code language model's representation space by clustering contextualized token embeddings into human-interpretable concept groups. We propose a hybri…

Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches
Kajwan Ziaoddini
https://arxiv.org/abs/2510.00006 https://a…

Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches
This paper examines how musical symbolism is produced and circulated in online communities by combining content-based music analysis with a lightweight network perspective on lyrics. Using a curated corpus of 275 chart-topping songs enriched with audio descriptors (energy, danceability, loudness, liveness, valence, acousticness, speechiness, popularity) and full lyric transcripts, we build a reproducible pipeline that (i) quantifies temporal trends in sonic attributes, (ii) models lexical salie…

Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo
https://arxiv.org/abs/2510.01002

Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
Current learning-based Automated Vulnerability Repair (AVR) approaches, while promising, often fail to generalize effectively in real-world scenarios. Our diagnostic analysis reveals three fundamental weaknesses in state-of-the-art AVR approaches: (1) limited cross-repository generalization, with performance drops on unseen codebases; (2) inability to capture long-range dependencies, causing a performance degradation on complex, multi-hunk repairs; and (3) over-reliance on superficial lexical p…

Beyond the Black Box: Integrating Lexical and Semantic Methods in Quantitative Discourse Analysis with BERTopic
Thomas Compton
https://arxiv.org/abs/2508.19099 https://

Beyond the Black Box: Integrating Lexical and Semantic Methods in Quantitative Discourse Analysis with BERTopic
Quantitative Discourse Analysis has seen growing adoption with the rise of Large Language Models and computational tools. However, reliance on black box software such as MAXQDA and NVivo risks undermining methodological transparency and alignment with research goals. This paper presents a hybrid, transparent framework for QDA that combines lexical and semantic methods to enable triangulation, reproducibility, and interpretability. Drawing from a case study in historical political discourse, we …

Storage places in diplomatic texts (7th-13th centuries). Lexical, semantic, and digital investigation
Nicolas Perreaux (LAMOP, CNRS)
https://arxiv.org/abs/2509.12230 https://

Storage places in diplomatic texts (7th-13th centuries). Lexical, semantic, and digital investigation
This study examines the evolution of references to grain storage structures in medieval European charters, based on a quantitative and semantic analysis of the digitized CEMA (Cartae Europae Medii Aevi) corpus comprising more than 225,000 documents. The author applies text mining and distributional analysis methods to a lexicon of some forty terms designating storage locations (grangia, horreum, granarium, granica, etc.), cross-referencing these data with references to grain and analyzing their…

Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Mengzhao Wang, Boyu Tan, Yunjun Gao, Hai Jin, Yingfeng Zhang, Xiangyu Ke, Xiaoliang Xu, Yifan Zhu
https://arxiv.org/abs/2508.01405

Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Hybrid search, the integration of lexical and semantic retrieval, has become a cornerstone of modern information retrieval systems, driven by demanding applications like Retrieval-Augmented Generation (RAG). The architectural design space for these systems is vast and complex, yet a systematic, empirical understanding of the trade-offs among their core components--retrieval paradigms, combination schemes, and re-ranking methods--is critically lacking. To address this, and informed by our experi…

Mind the Gap: Linguistic Divergence and Adaptation Strategies in Human-LLM Assistant vs. Human-Human Interactions
Fulei Zhang, Zhou Yu
https://arxiv.org/abs/2510.02645 https://

Mind the Gap: Linguistic Divergence and Adaptation Strategies in Human-LLM Assistant vs. Human-Human Interactions
As Large Language Models (LLMs) are increasingly deployed in customer-facing applications, a critical yet underexplored question is how users communicate differently with LLM chatbots compared to human agent. In this study, we present empirical evidence that users adopt distinct communication styles when users interact with chatbots versus human agents. Our analysis reveals significant differences in grammatical fluency, politeness, and lexical diversity in user language between the two setting…