Tootfinder

Explicit vs. Implicit Biographies: Evaluating and Adapting LLM Information Extraction on Wikidata-Derived Texts
Alessandra Stramiglio, Andrea Schimmenti, Valentina Pasqual, Marieke van Erp, Francesco Sovrano, Fabio Vitali
https://arxiv.org/abs/2509.14943

Explicit vs. Implicit Biographies: Evaluating and Adapting LLM Information Extraction on Wikidata-Derived Texts
Text Implicitness has always been challenging in Natural Language Processing (NLP), with traditional methods relying on explicit statements to identify entities and their relationships. From the sentence "Zuhdi attends church every Sunday", the relationship between Zuhdi and Christianity is evident for a human reader, but it presents a challenge when it must be inferred automatically. Large language models (LLMs) have proven effective in NLP downstream tasks such as text comprehension and infor…

Patent Language Model Pretraining with ModernBERT
Amirhossein Yousefiramandi, Ciaran Cooney
https://arxiv.org/abs/2509.14926 https://arxiv.org/pdf/2509.149…

Patent Language Model Pretraining with ModernBERT
Transformer-based language models such as BERT have become foundational in NLP, yet their performance degrades in specialized domains like patents, which contain long, technical, and legally structured text. Prior approaches to patent NLP have primarily relied on fine-tuning general-purpose models or domain-adapted variants pretrained with limited data. In this work, we pretrain 3 domain-specific masked language models for patents, using the ModernBERT architecture and a curated corpus of over …

Automated Triaging and Transfer Learning of Incident Learning Safety Reports Using Large Language Representational Models
Peter Beidler, Mark Nguyen, Kevin Lybarger, Ola Holmberg, Eric Ford, John Kang
https://arxiv.org/abs/2509.13706

Automated Triaging and Transfer Learning of Incident Learning Safety Reports Using Large Language Representational Models
PURPOSE: Incident reports are an important tool for safety and quality improvement in healthcare, but manual review is time-consuming and requires subject matter expertise. Here we present a natural language processing (NLP) screening tool to detect high-severity incident reports in radiation oncology across two institutions. METHODS AND MATERIALS: We used two text datasets to train and evaluate our NLP models: 7,094 reports from our institution (Inst.), and 571 from IAEA SAFRON (SF), all of …

Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data
Kurt Micallef, Nizar Habash, Claudia Borg
https://arxiv.org/abs/2509.12853 https://

Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data
Maltese is a unique Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. Despite its Semitic roots, its orthography is based on the Latin script, creating a gap between it and its closest linguistic relatives in Arabic. In this paper, we explore whether Arabic-language resources can support Maltese natural language processing (NLP) through cross-lingual augmentation techniques. We investigate multiple strategies for a…

Can maiBERT Speak for Maithili?
Sumit Yadav, Raju Kumar Yadav, Utsav Maskey, Gautam Siddharth Kashyap Md Azizul Hoque, Ganesh Gautam
https://arxiv.org/abs/2509.15048 https://

Can maiBERT Speak for Maithili?
Natural Language Understanding (NLU) for low-resource languages remains a major challenge in NLP due to the scarcity of high-quality data and language-specific models. Maithili, despite being spoken by millions, lacks adequate computational resources, limiting its inclusion in digital and AI-driven applications. To address this gap, we introducemaiBERT, a BERT-based language model pre-trained specifically for Maithili using the Masked Language Modeling (MLM) technique. Our model is trained on a…

AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
Xinxu Zhou, Jiaqi Bai, Zhenqi Sun, Fanxiang Zeng, Yue Liu
https://arxiv.org/abs/2509.13677

AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
Although significant progress has been made in many tasks within the field of Natural Language Processing (NLP), Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation. Additionally, in real scenario and online applications, cost considerations, scalability, domain knowledge learning and more precise control are required, presenting more challenge for CTG. This paper introduces a novel and scalable frame…

Advancing Conversational AI with Shona Slang: A Dataset and Hybrid Model for Digital Inclusion
Happymore Masoka
https://arxiv.org/abs/2509.14249 https://ar…

Advancing Conversational AI with Shona Slang: A Dataset and Hybrid Model for Digital Inclusion
African languages remain underrepresented in natural language processing (NLP), with most corpora limited to formal registers that fail to capture the vibrancy of everyday communication. This work addresses this gap for Shona, a Bantu language spoken in Zimbabwe and Zambia, by introducing a novel Shona--English slang dataset curated from anonymized social media conversations. The dataset is annotated for intent, sentiment, dialogue acts, code-mixing, and tone, and is publicly available at https…

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…

Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Junichiro Niimi
https://arxiv.org/abs/2508.11454 https://arxiv.org/pdf/2508.1145…

Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Large language models (LLMs) are now widely used across many fields, including marketing research. Sentiment analysis, in particular, helps firms understand consumer preferences. While most NLP studies classify sentiment from review text alone, marketing theories, such as prospect theory and expectation--disconfirmation theory, point out that customer evaluations are shaped not only by the actual experience but also by additional reference points. This study therefore investigates how the conte…

LLM Compression: How Far Can We Go in Balancing Size and Performance?
Sahil Sk, Debasish Dhal, Sonal Khosla, Sk Shahid, Sambit Shekhar, Akash Dhaka, Shantipriya Parida, Dilip K. Prasad, Ond\v{r}ej Bojar
https://arxiv.org/abs/2508.11318

LLM Compression: How Far Can We Go in Balancing Size and Performance?
Quantization is an essential and popular technique for improving the accessibility of large language models (LLMs) by reducing memory usage and computational costs while maintaining performance. In this study, we apply 4-bit Group Scaling Quantization (GSQ) and Generative Pretrained Transformer Quantization (GPTQ) to LLaMA 1B, Qwen 0.5B, and PHI 1.5B, evaluating their impact across multiple NLP tasks. We benchmark these models on MS MARCO (Information Retrieval), BoolQ (Boolean Question Answeri…