Large language models (LLMs) frequently exhibit performance biases against regional dialects of low-resource languages. However, frameworks to quantify these disparities remain scarce. We propose a two-phase framework to evaluate dialectal bias, operationalized as comprehension degradation relative to standard Bengali,...
K. M. Jubair Sami, Dipto Sumit, Ariyan Hossain et al.· 0 citations
A three-stage evaluation pipeline is designed that progressively reduces textual guidance to systematically evaluate LALMs' in-context learning ability in the audio modality, uncovering a consistent asymmetry across LALMs.
Yen-Ting Piao, Jay Chiehen Liao, W. Chien et al.· 1 citation
IndexRAG is presented, a novel approach that shifts cross-document reasoning from online inference to offline indexing and achieves the best average performance among all evaluated methods, including graph-based baselines such as HippoRAG2 and FastGraphRAG, while relying on a flat vector index.
Sampling from a categorical distribution is mathematically simple, but in large-vocabulary decoding, it often triggers extra memory traffic and extra kernels after the LM head. We present FlashSampling, an exact sampling primitive that fuses sampling into the LM-head matmul and never materializes the logits tensor in H...
Tomas Ruiz, Zhen Qin, Yifan Zhang et al.· 0 citations
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SalamahBench is introduced, a unified benchmark of 8{,}270 human-verified harmful prompts across ML Commons hazard categories, each rendered in Modern Standard Arabic (MSA) and five regional Arabic varieties and five regional Arabic varieties, for a total of 49{,}620 paired instances.
Omar Abdelnasser, Fatemah Alharbi, Khaled N. Khasawneh et al.· 0 citations
This study investigates the diverse characteristics of nouns, focusing on both semantic and morphosyntactic distinctions, and proves the model's representational completeness in learning and prediction and presents a unified framework for understanding the complexities of morphological markings across languages.
This study provides new mechanistic insights into how LLMs internalize OOD challenges, and designs Sparsity-Guided Curriculum In-Context Learning (SG-ICL), a strategy that explicitly uses representation sparsity to schedule few-shot demonstrations, leading to considerable performance enhancements.
A fully automated annotation framework for generating precise molecular descriptions at scale, such that the original molecule can be unambiguously reconstructed from the description alone and the proposed framework and dataset provide a reliable foundation for molecule--language alignment.
Time-Annealed Perturbation Sampling (TAPS) is proposed, a training-free inference strategy that samples nearby conditioning trajectories through time-aware, manifold-constrained perturbations, and consistently improves semantic and lexical diversity across open-ended and instruction-following generation tasks.
Jing-Xuan Wu, Zheng-Lin Wan, Xing-Rui Yu et al.· 4 citations
Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set, and it is found that models robustness in HRL setting do not necessarily translate to LRL.
Tian-Yi Xu, Kosei Uemura, Alfred Malengo Kondoro et al.· arXiv.org· 4 citations
This work proposes \textbf{Self-ReCheck}, a lightweight, model-agnostic memory filtering mechanism that mitigates over-personalization while preserving personalization performance in memory-augmented dialogue systems.
Synthetic data enables us to construct diverse, controlled inputs, and it reliably reproduces bias patterns observed in natural text, supporting its use for large-scale analysis and highlighting the need for systematic bias auditing before deploying LLMs in high-stakes applications.
Akram Elbouanani, Aboubacar Tuo, Adrian Popescu· Annual Meeting of the Associ...· 3 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.
