arXiv:2501.16836v2 Announce Type: replace-cross Abstract: This survey provides an overview of the challenges of misspellings in natural language processing (NLP). While often unintentional, misspellings have become ubiquitous in digital communication, especially with the proliferation of Web 2.0, user-generated content, and informal text mediums such as social media, blogs, and forums. Even if humans can generally […]
Correlation Dimension of Auto-Regressive Large Language Models
arXiv:2510.21258v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in natural language generation, yet they continue to display puzzling behaviors — such as repetition and incoherence — even when exhibiting low perplexity. This highlights a key limitation of conventional evaluation metrics, which emphasize local prediction accuracy while overlooking long-range structural complexity. […]
Customizing Open Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems
arXiv:2510.21027v1 Announce Type: new Abstract: Harmonizing medication data across Electronic Health Record (EHR) systems is a persistent barrier to monitoring medications for opioid use disorder (MOUD). In heterogeneous EHR systems, key prescription attributes are scattered across differently formatted fields and freetext notes. We present a practical framework that customizes open source large language models (LLMs), […]
Efficient semantic uncertainty quantification in language models via diversity-steered sampling
arXiv:2510.21310v1 Announce Type: cross Abstract: Accurately estimating semantic aleatoric and epistemic uncertainties in large language models (LLMs) is particularly challenging in free-form question answering (QA), where obtaining stable estimates often requires many expensive generations. We introduce a diversity-steered sampler that discourages semantically redundant outputs during decoding, covers both autoregressive and masked diffusion paradigms, and yields […]
Seeing Sound, Hearing Sight: Uncovering Modality Bias and Conflict of AI models in Sound Localization
arXiv:2505.11217v2 Announce Type: replace-cross Abstract: Imagine hearing a dog bark and turning toward the sound only to see a parked car, while the real, silent dog sits elsewhere. Such sensory conflicts test perception, yet humans reliably resolve them by prioritizing sound over misleading visuals. Despite advances in multimodal AI integrating vision and audio, little is […]
Weak-to-Strong Generalization under Distribution Shifts
arXiv:2510.21332v1 Announce Type: cross Abstract: As future superhuman models become increasingly complex, accurately supervising their behavior may exceed human capabilities. Recent works have demonstrated that in such scenarios, weak models can effectively supervise strong models, a phenomenon known as weak-to-strong generalization. However, we find that naive weak-to-strong generalization fails under distribution shifts, often leading to […]
Epistemic Deference to AI
arXiv:2510.21043v1 Announce Type: new Abstract: When should we defer to AI outputs over human expert judgment? Drawing on recent work in social epistemology, I motivate the idea that some AI systems qualify as Artificial Epistemic Authorities (AEAs) due to their demonstrated reliability and epistemic superiority. I then introduce AI Preemptionism, the view that AEA outputs […]
CT-CLIP: A Multi-modal Fusion Framework for Robust Apple Leaf Disease Recognition in Complex Environments
arXiv:2510.21346v1 Announce Type: cross Abstract: In complex orchard environments, the phenotypic heterogeneity of different apple leaf diseases, characterized by significant variation among lesions, poses a challenge to traditional multi-scale feature fusion methods. These methods only integrate multi-layer features extracted by convolutional neural networks (CNNs) and fail to adequately account for the relationships between local and […]
AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
arXiv:2505.18512v2 Announce Type: replace-cross Abstract: Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications. Due to the limit in context size and high inference cost of long context, reranking is typically performed over a fixed size of small subsets, with the final ranking aggregated from these partial results. This fixed computation […]
Compressing Quaternion Convolutional Neural Networks for Audio Classification
arXiv:2510.21388v1 Announce Type: cross Abstract: Conventional Convolutional Neural Networks (CNNs) in the real domain have been widely used for audio classification. However, their convolution operations process multi-channel inputs independently, limiting the ability to capture correlations among channels. This can lead to suboptimal feature learning, particularly for complex audio patterns such as multi-channel spectrogram representations. Quaternion […]