Tootfinder

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
Rui Zhao, Vladyslav Melnychuk, Jun Zhao, Jesse Wright, Nigel Shadbolt
https://arxiv.org/abs/2507.14214

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with t…

:sitake:

DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
Yaxin Gao, Yao Lu, Zongfei Zhang, Jiaqi Nie, Shanqing Yu, Qi Xuan
https://arxiv.org/abs/2509.13723

DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
Large language models (LLMs) have achieved remarkable success in many natural language processing (NLP) tasks. To achieve more accurate output, the prompts used to drive LLMs have become increasingly longer, which incurs higher computational costs. To address this prompt inflation problem, prompt compression has been proposed. However, most existing methods require training a small auxiliary model for compression, incurring a significant amount of additional computation. To avoid this, we propo…

Bhaasha, Bhasa, Zaban: A Survey for Low-Resourced Languages in South Asia -- Current Stage and Challenges
Sampoorna Poria, Xiaolei Huang
https://arxiv.org/abs/2509.11570 https:/…

Bhaasha, Bhasa, Zaban: A Survey for Low-Resourced Languages in South Asia - Current Stage and Challenges
Rapid developments of large language models have revolutionized many NLP tasks for English data. Unfortunately, the models and their evaluations for low-resource languages are being overlooked, especially for languages in South Asia. Although there are more than 650 languages in South Asia, many of them either have very limited computational resources or are missing from existing language models. Thus, a concrete question to be answered is: Can we assess the current stage and challenges to info…

A Data-constrained Magnetohydrodynamic Simulation of Successive X-class Flares in Solar Active Region 13842. II. Dynamics of the Solar Eruption Associated with the X9.0 Solar Flare
Keitarou Matsumoto, Satoshi Inoue, Keiji Hayashi, Nian Liu, Ying Wang, Jeongwoo Lee, Ju Jing, Haimin Wang
https://arxiv.org/abs/2508.07988

A Data-constrained Magnetohydrodynamic Simulation of Successive X-class Flares in Solar Active Region 13842. II. Dynamics of the Solar Eruption Associated with the X9.0 Solar Flare
Active region NOAA 13842 produced two successive solar flares: an X7.1-class flare on October 1, 2024, and an X9.0-class flare on October 3, 2024. This study continues our previous simulation work that successfully reproduced the X7.1-class solar flare (Matsumoto et al. 2025). In this study, we performed a data-constrained magnetohydrodynamic (MHD) simulation using the nonlinear force-free field (NLFFF) as the initial condition to investigate the X9.0-class solar flare. The NLFFF showed the she…

Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Gokul Karthik Kumar, Rishabh Saraf, Ludovick Lepauloux, Abdul Muneer, Billel Mokeddem, Hakim Hacid
https://arxiv.org/abs/2509.07526

Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Large language models (LLMs) have transformed NLP, yet their integration with audio remains underexplored -- despite audio's centrality to human communication. We introduce Falcon3-Audio, a family of Audio-Language Models (ALMs) built on instruction-tuned LLMs and Whisper encoders. Using a remarkably small amount of public audio data -- less than 30K hours (5K unique) -- Falcon3-Audio-7B matches the best reported performance among open-weight models on the MMAU benchmark, with a score of 64.14,…

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval
Zihan Chen, Lei Shi, Weize Wu, Qiji Zhou, Yue Zhang
https://arxiv.org/abs/2509.07512

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval
Many contemporary data-driven research efforts in the natural sciences, such as chemistry and materials science, require large-scale, high-performance entity recognition from scientific datasets. Large language models (LLMs) have increasingly been adopted to solve the entity recognition task, with the same trend being observed on all-spectrum NLP tasks. The prevailing entity recognition LLMs rely on fine-tuned technology, yet the fine-tuning process often incurs significant cost. To achieve a b…

Polarity Detection of Sustainable Detection Goals in News Text
Andrea Cadeddua, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Francesco Osborne, Diego Reforgiato Recupero, Angelo Salatino, Luca Secchi
https://arxiv.org/abs/2509.19833

Polarity Detection of Sustainable Detection Goals in News Text
The United Nations' Sustainable Development Goals (SDGs) provide a globally recognised framework for addressing critical societal, environmental, and economic challenges. Recent developments in natural language processing (NLP) and large language models (LLMs) have facilitated the automatic classification of textual data according to their relevance to specific SDGs. Nevertheless, in many applications, it is equally important to determine the directionality of this relevance; that is, to assess…

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation
Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang
https://arxiv.org/abs/2507.21028

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation
Nearly all human work is collaborative; thus, the evaluation of real-world NLP applications often requires multiple dimensions that align with diverse human perspectives. As real human evaluator resources are often scarce and costly, the emerging "LLM-as-a-judge" paradigm sheds light on a promising approach to leverage LLM agents to believably simulate human evaluators. Yet, to date, existing LLM-as-a-judge approaches face two limitations: persona descriptions of agents are often arbitrarily de…