Social media enables cross-lingual communication, yet the meaning of user-generated content (UGC) is often distributed across text, images, and cultural knowledge, making translation challenging. Existing evaluations typically treat images as optional and assume textual information is sufficient, obscuring whether erro...
Dingming Li, Yiwen Qiu, Linjuan Wu et al.· 0 citations
Constrained decoding guarantees that Large Language Models (LLMs) produce outputs conforming to predefined formats, but is often assumed to inherently degrade reasoning. We examine this assumption through a taxonomy of three constraint types: \emph{format constraints}, which enforce output structure; \emph{validity con...
Ngoc Trinh Hung Nguyen, Alonso Silva, Armen Aghasaryan et al.· 0 citations
A fundamental challenge for explainable and accountable language models is revealing how external information influences their answers. This paper examines whether models are self-attributive: that is, whether they indicate when -- and only when -- their responses to questions are influenced by information provided alo...
Zing Z. X. Zhou, Jia-Cheng Sang, Yi-Lin Xu et al.· 0 citations
The Conflict of Skills Attack (CoSA) is introduced, which adds locally admitted Skills to an existing pool to supply missing business operations or reshape artifact exchange, decision reuse, and target/action bindings (relationship reshaping).
Yu-Jian Zheng, Tian-Hao Li, Shang-Zhi Xu et al.· 0 citations
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GLP-1 medications are increasingly used for weight management, and many people share their experiences online. These narratives show why people take the drugs and what they experience, but they remain unstructured and disconnected from formal pharmacovigilance. We therefore present AskGLP1, a system that transforms onl...
Zhaoqian Xue, Anay Somani, Siddhartha Singh et al.· 0 citations
Pretrained transformers spend a large share of their inference compute inside self-attention, yet the per-head query-key interaction is structurally low-rank by construction. We ask a diagnostic question: across layers and architectures, which heads actually use the full rank budget on real data, and which do not? We p...
Debopam Sanyal, Hongjie Chen, Riya Elizabeth Kuriyan et al.· 0 citations
A numerical claim generated by a financial analyst agent can match a number in a company filing but refer to the wrong entity, concept, period, unit or scope. A check based on reference answers can mark such an answer as wrong but does not explain why it fails. In this paper, we present NumTrace, a new benchmark that a...
Tian-Lei Zhu, Matt White, Xue Liu et al.· 0 citations
Retrieval-augmented generation (RAG) can improve factual grounding without guaranteeing that every generated claim is supported. Previous work has studied claim-level conformal factuality in single-stage RAG and, more recently, multi-hop RAG. We examine its behavior in agentic RAG, where a language-model policy adaptiv...
Knowledge editing aims to update outdated or incorrect knowledge in large language models without full retraining. Existing parameter-editing methods remain limited for long-sequence knowledge: single-point editors provide insufficient coverage for extended targets, while fixed chunk-based editors distribute editing ef...
Jin-Hu Fu, Yan Bai, Yihang Lou et al.· 0 citations
Entity matching is the task of deciding whether two records describe the same real-world entity. Current entity matching methods rely on pre-trained language models (PLMs) which require task specific fine-tuning or generative large language models (LLMs) which do not require task-specific fine-tuning but are slow and e...
Aaron Steiner, Ralph Peeters, Christian Bizer· 0 citations
Tabular data is widely used in scientific applications, where complex layouts, including multi-level headers, merged cells, captions, and footnotes, make information extraction difficult. This is particularly important in pharmacokinetics (PK), where values must be correctly associated with the drug, species, dose, adm...
Hossein Sholehrasa, Mohammadhossein Amini, Doina Caragea et al.· 0 citations
The article starts with the Transformer, which connects words in context, and pre-training, where models learn to predict text, and explains how fine-tuning adapts models to requests, with reinforcement learning from human feedback using human answer rankings.
