Tootfinder

Worker Pool Design Pattern Explanation
https://coffeebytes.dev/en/software-architecture/worker-pool-design-pattern-explanation/

MATA (m\=ata): Mindful Assessment of the Telugu Abilities of Large Language Models
Chalamalasetti Kranti, Sowmya Vajjala
https://arxiv.org/abs/2508.13526 https://

MATA (māta): Mindful Assessment of the Telugu Abilities of Large Language Models
In this paper, we introduce MATA, a novel evaluation dataset to assess the ability of Large Language Models (LLMs) in Telugu language, comprising 729 carefully curated multiple-choice and open-ended questions that span diverse linguistic dimensions. We evaluate 11 open-weight and closed-source LLMs on our dataset and present a fine-grained analysis of their performance. Further, we empirically show how LLMs rely on superficial heuristics such as answer position and distractor patterns for multi…

Pattern matching is a major feature in software development. While pattern matching applies in several locations, its current usage is limited to switch case blocks. I want to compare the power of pattern matching across a couple of programming languages I’m familiar with in this post.
https://blo…

DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
Yitong Li, Raoul Grasman
https://arxiv.org/abs/2507.13957 …

DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
The modern recommender systems are facing an increasing challenge of modelling and predicting the dynamic and context-rich user preferences. Traditional collaborative filtering and content-based methods often struggle to capture the temporal patternings and evolving user intentions. While Large Language Models (LLMs) have gained gradual attention in recent years, by their strong semantic understanding and reasoning abilities, they are not inherently designed to model chronologically evolving us…

"Can You See Me Think?" Grounding LLM Feedback in Keystrokes and Revision Patterns
Samra Zafar, Shifa Yousaf, Muhammad Shaheer Minhas
https://arxiv.org/abs/2508.13543 …

"Can You See Me Think?" Grounding LLM Feedback in Keystrokes and Revision Patterns
As large language models (LLMs) increasingly assist in evaluating student writing, researchers have begun questioning whether these models can be cognitively grounded, that is, whether they can attend not just to the final product, but to the process by which it was written. In this study, we explore how incorporating writing process data, specifically keylogs and time-stamped snapshots, affects the quality of LLM-generated feedback. We conduct an ablation study on 52 student essays comparing f…

Generics and Default Reasoning in Large Language Models
James Ravi Kirkpatrick, Rachel Katharine Sterken
https://arxiv.org/abs/2508.13718 https://arxiv.org…

Generics and Default Reasoning in Large Language Models
This paper evaluates the capabilities of 28 large language models (LLMs) to reason with 20 defeasible reasoning patterns involving generic generalizations (e.g., 'Birds fly', 'Ravens are black') central to non-monotonic logic. Generics are of special interest to linguists, philosophers, logicians, and cognitive scientists because of their complex exception-permitting behaviour and their centrality to default reasoning, cognition, and concept acquisition. We find that while several frontier mode…

Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies
Carlos Mena, Pol Serra, Jacobo Romero, Abir Messaoudi, Jose Giraldo, Carme Armentano-Oller, Rodolfo Zevallos, Ivan Meza, Javier Hernando
https://arxiv.org/abs/2507.13875

Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies
Code-switching (CS), the alternating use of two or more languages, challenges automatic speech recognition (ASR) due to scarce training data and linguistic similarities. The lack of dedicated CS datasets limits ASR performance, as most models rely on monolingual or mixed-language corpora that fail to reflect real-world CS patterns. This issue is critical in multilingual societies where CS occurs in informal and formal settings. A key example is Catalan-Spanish CS, widely used in media and parli…

The Expressions of Depression and Anxiety in Chinese Psycho-counseling: Usage of First-person Singular Pronoun and Negative Emotional Words
Lizhi Ma, Tong Zhao, Shuai Zhang, Nirui Song, Hongliang He, Anqi Li, Ran Feng, Huachuan Qiu, Jingsong Ma, Zhenzhong Lan
https://arxiv.org/abs/2507.13839…

The Expressions of Depression and Anxiety in Chinese Psycho-counseling: Usage of First-person Singular Pronoun and Negative Emotional Words
This study explores the relationship between linguistic expressions and psychological states of depression and anxiety within Chinese psycho-counseling interactions, focusing specifically on the usage of first-person singular pronouns and negative emotional words. Utilizing a corpus derived from 735 online counseling sessions, the analysis employed a general linear mixed-effect model to assess linguistic patterns quantified by the Linguistic Inquiry and Word Count (LIWC) software. Results indic…

A Comparative Evaluation of Large Language Models for Persian Sentiment Analysis and Emotion Detection in Social Media Texts
Kian Tohidi, Kia Dashtipour, Simone Rebora, Sevda Pourfaramarz
https://arxiv.org/abs/2509.14922

A Comparative Evaluation of Large Language Models for Persian Sentiment Analysis and Emotion Detection in Social Media Texts
This study presents a comprehensive comparative evaluation of four state-of-the-art Large Language Models (LLMs)--Claude 3.7 Sonnet, DeepSeek-V3, Gemini 2.0 Flash, and GPT-4o--for sentiment analysis and emotion detection in Persian social media texts. Comparative analysis among LLMs has witnessed a significant rise in recent years, however, most of these analyses have been conducted on English language tasks, creating gaps in understanding cross-linguistic performance patterns. This research ad…

RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang, Runzhe Zhan, Zeyu Wu, Ziyang Luo, Di Wang, Min Yang, Lidia S. Chao, Derek F. Wong
https://arxiv.org/abs/2508.13152

RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well, their robustness in out-of-distribution (OOD) scenarios is still lacking. In this paper, we hypothesize that, compared to features used by existing detection methods, the internal representations of LLMs contain more comprehensive and raw features that can more effectively capture and distinguish the statistical patte…