Quality follows upgrading
Academic
arXiv:2602.12778v1 Announce Type: new Abstract: This study advances aspect-based sentiment analysis (ABSA) for Persian-language user reviews in the tourism domain, addressing challenges of low-resource languages. …
arXiv:2602.12806v1 Announce Type: new Abstract: Data containing personal information is increasingly used to train, fine-tune, or query Large Language Models (LLMs). Text is typically scrubbed …
arXiv:2602.12811v1 Announce Type: new Abstract: When humans and large language models (LLMs) process the same text, activations in the LLMs correlate with brain activity measured, …
arXiv:2602.12818v1 Announce Type: new Abstract: Detecting reclaimed slurs represents a fundamental challenge for hate speech detection systems, as the same lexcal items can function either …
arXiv:2602.12871v1 Announce Type: new Abstract: We introduce MentalBench, a benchmark for evaluating psychiatric diagnostic decision-making in large language models (LLMs). Existing mental health benchmarks largely …
arXiv:2602.12889v1 Announce Type: new Abstract: We present BaziQA-Benchmark, a standardized benchmark for evaluating symbolic and temporally compositional reasoning in large language models. The benchmark is …
arXiv:2602.12911v1 Announce Type: new Abstract: Code-switching (CS), which is when Vietnamese speech uses English words like drug names or procedures, is a common phenomenon in …
arXiv:2602.12921v1 Announce Type: new Abstract: Figurative language understanding remains a significant challenge for Large Language Models (LLMs), especially for low-resource languages. To address this, we …
arXiv:2602.12937v1 Announce Type: new Abstract: Being modeled as a single-label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) …
arXiv:2602.12966v1 Announce Type: new Abstract: Understanding how and why large language models (LLMs) fail is becoming a central challenge as models rapidly evolve and static …
arXiv:2602.12989v1 Announce Type: new Abstract: Keyphrases which are useful in several NLP and IR applications are either extracted from text or predicted by generative models. …
arXiv:2602.12996v1 Announce Type: new Abstract: Knowledge augmentation has significantly enhanced the performance of Large Language Models (LLMs) in knowledge-intensive tasks. However, existing methods typically operate …