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arXiv:2604.03058v1 Announce Type: new Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing …
arXiv:2604.03057v1 Announce Type: new Abstract: This paper presents an open source methodology for allowing users to query structured non textual datasets through natural language Unlike …
arXiv:2604.03004v1 Announce Type: new Abstract: While deep reasoning with long chain-of-thought has dramatically improved large language models in verifiable domains like mathematics, its effectiveness for …
arXiv:2604.02972v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have recently achieved remarkable success in complex reasoning tasks. However, closer scrutiny reveals persistent failure modes …
arXiv:2604.02954v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge …
arXiv:2604.02926v1 Announce Type: new Abstract: The article proposes a new architecture based on Multi-head attention to solve the problem of morphological tagging for the Russian …
arXiv:2604.02923v1 Announce Type: new Abstract: Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities across diverse natural language processing tasks. …
arXiv:2604.02904v1 Announce Type: new Abstract: In this article, we present a gold-standard benchmark dataset for Biomedical Urdu Named Entity Recognition (BioUNER), developed by crawling health-related …
arXiv:2604.02881v1 Announce Type: new Abstract: Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While …
arXiv:2604.02866v1 Announce Type: new Abstract: Knowledge Graph construction from natural language requires extracting structured triplets from complex, information-dense sentences. In this paper, we investigate if …
arXiv:2604.02830v1 Announce Type: new Abstract: Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying …
arXiv:2604.02819v1 Announce Type: new Abstract: Large reasoning models achieve strong performance on complex tasks through long chain-of-thought (CoT) trajectories, but directly transferring such reasoning processes …