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Newest papers, page 226
From annotation to reasoning: Culture in language models
How should we evaluate language models when more than one interpretation can be right?ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning
Large language models (LLMs) excel at natural language processing but struggle to interact with external environments.Cross-Backend QIEO: Universal Runtime Portability across OpenMP5, CUDA, HIP, and Multi-Language Interfaces
Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on classical hardware by representing candidate…Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring
Accurate and efficient pronunciation transcription is essential for preparing text-to-speech training data at scale.Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models
Continual fine-tuning is essential for large language models (LLMs) to dynamically adapt to real-world environments, yet it inevitably suffers from catastrophic forgetting, particularly the…FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation
Large language models are increasingly used as personalized writing assistants, but adapting a model across many authors can compromise individual writing style by pulling author-specific signals…Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change
Lexical semantic change is usually summarized by a scalar distance between independently sampled period distributions.Does Uniform Discrete Diffusion Need Time?
Uniform discrete diffusion models (UDMs) commonly use explicit time conditioning, but we find that it can often be unnecessary in practice.THA: Weighted Finite-State Text Normalization and Inverse Text Normalization for Khmer
Text-to-speech needs written text in spoken form, and speech recognition output needs the reverse.Evaluating Sycophancy in Chinese Large Language Models on Factual Questions Derived from Online Search Queries
As large language models increasingly mediate information access, factually accurate and independent answers are critical.ZooWork-ShopRanker: An Open, Preference-Aligned E-Commerce Reranker
Open rerankers trained for general web retrieval transfer imperfectly to e-commerce, where ranking decisions depend not only on topical relevance but also on user preferences, product constraints…G$^2$PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation
Post-training quantization (PTQ) is a practical approach to reducing the memory and computational footprint of large language models (LLMs) without retraining.Same Text, Different Numbers: The Divergence of LLM-Based Measures
Researchers increasingly use generative large language models (LLMs) to convert corporate text into empirical variables.KuaFu: Compressing Long User Behavior into Understanding at Billion Scale
Conversational agents, generative recommenders, and personalized advertising all rest on one capability: understanding each user from raw behavior.Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference
Collaborative science learning requires nuanced interpretation of student dialogue to characterize how learners identify knowledge gaps, build explanations, and work toward resolution - a…CG-Probes: Recovering Guardrail Directions from Patient Query Embeddings
Patient-facing AI assistants promise valuable support to patients, but incoming queries can pose medical risks.LocUS: Head Selection and Subspace Projection for Targeted Activation Steering
Activation steering is a powerful training-free paradigm for controlling large language models at inference time.Do we need to answer that question? Salience and Answerability of Potential Questions in Naturalistic Dialogue
We empirically investigate Question Under Discussion based modelling in naturalistic dialogue by studying whether the salience of generated potential questions predicts their subsequent resolution.JevAdvBench: A Benchmark and Black-Box Attacks for Reinforcement Learning for Calibrated Decisions Models
Models trained with reinforcement learning for calibrated decisions (RLCD), such as Jev, answer a typed question about an input, the state, with a probability, a choice, or a score, and software…Improving Visual Sensitivity of LLMs on Multimodal Machine Translation with Metric-based Loss Weighting
Multimodal Machine Translation aims to incorporate additional signal from non-textual modalities to improve translations by resolving ambiguities.