Vision-language models (VLMs), like human learners, are frequently exposed to new visual concepts, but how they map novel visual references to language after exposure remains largely underexplored, particularly when those references contradict prior knowledge from pre-training. To study this, we present the Novel Visua...
Ada Defne T\"ur, Gaurav Kamath, Joyce Chai et al.· 0 citations
As a modern commodity, language has become a vast repository of socially and psychologically significant traits and concepts, reflecting the ways people encode pattern of thoughts, behaviors, and emotions into words. Text-based Automatic Personality Prediction (APP), seeks to infer personality from linguistic behavior,...
Uncertainty estimation (UE) aims to quantify uncertainty in large language model (LLM) outputs to flag potential hallucinations and support reliable deployment. However, UE metrics often exhibit unstable performance across models and datasets, limiting their practical utility. We formalise this phenomenon as proxy fail...
Referring understanding is a fundamental task that bridges natural language and visual content by localizing objects described in free-form expressions. However, existing works are constrained by limited language expressiveness, lacking the capacity to model object dynamics in spatial numbers and temporal states. To ad...
Yani Zhang, Dongming Wu, Wencheng Han et al.· 0 citations
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Transferable attacks allow adversarial manipulations or learned attack mechanisms constructed in one setting to compromise other AI systems, potentially reducing attack costs and amplifying security and privacy risks. Yet fragmented terminology and evaluation practices hinder understanding of what transfers, why it tra...
Guangjing Wang, Ce Zhou, Yuanda Wang et al.· 0 citations
We present FrenchNews-7, a cross-publisher France-based French-language news editorial desk classification benchmark combining a large multi-outlet corpus, a URL-derived seven-class taxonomy, and a fine-tuned CamemBERT classifier. Labels are assigned via a hybrid pipeline combining publisher URL slugs with LLM annotati...
AIMS, a human-annotated dataset of 1,724 difficult safety prompts, is introduced to evaluate intent-aware training across supervised fine-tuning, preference learning, reasoning distillation, and reinforcement learning and shows that faithful intent modeling is a compact, high-quality supervision signal for more robust...
J. Ferrao, Niclas Müller-Hof, Iustin Sîrbu et al.· arXiv.org· 1 citation
AgentOdyssey is introduced, a novel evaluation framework that procedurally generates open-ended text games with rich entities, world dynamics, and long-horizon tasks and proposes a multifaceted evaluation methodology that measures not only game progress but also offers diagnostic tests on world knowledge acquisition, e...
Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous...
Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen et al.· 0 citations
Natural language understanding often depends on meanings that are implied rather than explicitly stated, requiring pragmatic reasoning. Despite strong performance on math and logical reasoning, large language models (LLMs) still struggle with making pragmatic inferences, often choosing literal interpretations. To impro...
Jihyung Park, Minchao Huang, Leqi Liu et al.· 0 citations
We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept. We propose TimpaTeks, an automatic in-place text modification mechanism using DLMs. Experiments on IMDB movie reviews...
Ryandito Diandaru, Ahmed Elshabrawy, Fadli Aulawi Al Ghiffari et al.· 0 citations
Multilingual large language models (MLLMs) often answer culturally ambiguous questions inconsistently across languages, even for a fixed user persona -- e.g., Shakespeare in English but Cervantes in Spanish for the same British user. To robustly quantify this Cross-lingual Cultural Inconsistency, we introduce Singleton...
Lucas Resck, Isabelle Augenstein, Anna Korhonen· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.