Natural Language Processing

A new data-free knowledge distillation framework called Gradient Transformer enables organizations with limited computational resources to improve large language models (LLMs) using private data without sharing that data…

Researchers have developed Soro, a family of Tajik-specialized conversational AI models built from Gemma 3 checkpoints and trained on a 1.9-billion-token Tajik corpus. The models outperform same-size baselines on new Taj…

Researchers have developed EvoSpec, a framework that enables real-time evolution of draft models in speculative decoding through dynamic vocabulary and parameter adaptation. The system addresses the failure of static pru…

Researchers have proposed a unified full-lifecycle governance framework for cyberbullying on social media, shifting from passive, post-level detection to integrated, continuous, and proactive moderation. The framework sy…

Researchers have developed a new approach enabling AI agents to reason and plan using dynamically changing social norms, addressing a gap in human-AI interaction safety. The method introduces a defeasible calculus for re…

Researchers have developed a hierarchical control-and-learning framework for large language models deployed in resource-constrained agentic systems, addressing failures caused by prompt extension beyond a model's effecti…

Researchers have developed DeepSciVerify, a two-stage pipeline that verifies scientific claim-citation alignment by first analyzing abstracts and escalating uncertain cases to full-text evidence only when needed. The sys…

Researchers have developed techniques to enable fine-grained and within-utterance speaking style control in prompt-based text-to-speech models. The methods allow smooth style interpolation between utterances and time-var…

Researchers have developed RAG-Coding, a method that uses four large language model agents to automate ICD-10-CM medical coding by grounding decisions in official coding tabular lists and guidelines. On the MDACE dataset…

Researchers have developed OralAgent, the first dental-specialized AI agent that integrates 22 visual analysis tools and 368 dental textbooks to autonomously perform multimodal reasoning, tool-based decision-making, and …

Researchers at the University of Cambridge have identified a fundamental trade-off between stability and expressivity in spoken language models trained on synthetic data, where phonetic accuracy improves but prosodic var…

Researchers have developed a new interpretation method for Transformer models that use heterogenous attention structures, which process information from different sources. The approach addresses challenges in understandi…

Researchers evaluating English-to-Hindi translation systems found that five major generative models frequently erase gender cues through ergative and honorific constructions, failing to preserve gender recoverability fro…

Researchers developed a translation model that converts soft prompts—parameter-efficient but uninterpretable LLM adaptations—into natural language text. The translated prompts outperformed existing interpretability metho…

Researchers analyzed 703,975 de-identified Kids Help Phone conversations from 2018-2023 and expanded the platform's 19-label issue taxonomy into a 39-label hierarchical schema. They introduced Keyphrase Generative Repres…

Researchers have developed ForestHG-Trace, a framework enabling traceable long-horizon ecological reasoning over large-scale forest scenes by representing multimodal NEON forest data as ecological hypergraphs. The system…

Researchers introduced MTM-Bench, a controlled benchmark that isolates three distinct language roles—instruction, content, and response—across English, Spanish, and Chinese to evaluate multilingual LLM task execution. Te…

Researchers have developed FLUID, a framework that adapts autoregressive large language models for efficient parallel text generation using diffusion models. By enforcing Strictly Causal Alignment and Elastic Horizons, F…

Researchers have developed BioELX, a two-stage cross-lingual biomedical entity linking framework that requires no task-specific annotated training data. The system improves candidate retrieval by enriching SapBERT with m…

A developer exploring sparse embeddings for retrieval-augmented generation (RAG) detailed the progression from term frequency (TF) to inverse document frequency (IDF) and TF-IDF, culminating in the BM-25 algorithm. The e…