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arXiv:2602.18652v1 Announce Type: new Abstract: Multimodal models struggle with idiomatic expressions due to their non-compositional meanings, a challenge amplified in multilingual settings. We introduced PolyFrame, …
arXiv:2602.18692v1 Announce Type: new Abstract: Anxiety is the unease about a possible future negative outcome. In recent years, there has been growing interest in understanding …
arXiv:2602.18693v1 Announce Type: new Abstract: The spread of misinformation across digital platforms can pose significant societal risks. Claim verification, a.k.a. fact-checking, systems can help identify …
arXiv:2602.18699v1 Announce Type: new Abstract: Most semantic drift studies report multiple signals e.g., embedding displacement, neighbor changes, distributional divergence, and recursive trajectory instability, without a …
arXiv:2602.18721v1 Announce Type: new Abstract: Semi-supervised learning in automatic speech recognition (ASR) typically relies on pseudo-labeling, which often suffers from confirmation bias and error accumulation …
arXiv:2602.18734v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success, …
arXiv:2602.18776v1 Announce Type: new Abstract: We present ArabicNumBench, a comprehensive benchmark for evaluating large language models on Arabic number reading tasks across Eastern Arabic-Indic numerals …
arXiv:2602.18788v1 Announce Type: new Abstract: We introduce BURMESE-SAN, the first holistic benchmark that systematically evaluates large language models (LLMs) for Burmese across three core NLP …
arXiv:2602.18806v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate strong reasoning performance, yet their ability to reliably monitor, diagnose, and correct their own errors …
arXiv:2602.18823v1 Announce Type: new Abstract: Robust and comprehensive evaluation of large language models (LLMs) is essential for identifying effective LLM system configurations and mitigating risks …
arXiv:2602.18920v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) in accelerating scientific discovery has garnered increasing attention, with a key focus on …