Natural Language Processing

Researchers introduced RAD3D-Prefix, a lightweight diagnostic-prior conditioning framework for volumetric CT report generation that keeps large language models frozen to minimize overfitting and clinical hallucination. A…

Researchers at arXiv propose a soft prompting approach to improve language adherence in multimodal LLMs for ASR, introducing a metric to quantify violations and evaluating zero-shot prompting, supervised fine-tuning, and…

Researchers at arXiv have developed five verbalized methods to extract per-token confidence from large language models (LLMs) in machine translation, finding that these methods perform similarly to internal signals for e…

Researchers propose self-generated token-to-token (T2T) editing for diffusion language models, addressing a training-inference mismatch where the editor sees the model's own fluent draft errors instead of random corrupti…

Researchers at arXiv challenge the common assumption that visual attention correlates with reliability in vision-language models. Their VLM Reliability Probe study across multiple models finds that spatial attention metr…

Researchers propose structural uncertainty, a framework that measures consistency in LLM logical reasoning by analyzing self-preference-induced rankings over sampled solutions. Across five LLMs and eight benchmarks, comb…

Researchers introduced PromptMN, a pseudo-prompting domain-specific language that annotates natural language with compact typed directives to reduce context ambiguities in human-AI interactions. The language was evaluate…

Researchers at arXiv analyzed 4,434 posts and 50,338 comments from the Moltbook community to study parasocial interaction cues among autonomous AI agents. They found that attachment language, reciprocity bids, and self-i…

Researchers propose MemSlides, a hierarchical memory framework for personalized presentation generation that separates long-term user profiles, session-level working memory, and reusable tool memory to enable stable pers…

Researchers introduced DriveJudge, a driving evaluation agent that combines rule-grounded evaluation with Vision-Language Model (VLM) reasoning to assess autonomous driving in a context-aware and interpretable manner. Dr…

Researchers propose OR3, a text-to-video retrieval method for operating room clips that converts videos into action-driven digital twins and uses an LLM to generate hypothetical queries for intra-modal matching. The meth…

Researchers introduced a reinforcement learning framework that trains large language models to decouple perception from reasoning by operating over digital twin representations for surgical video question answering, achi…

Researchers introduced PuMVR, a benchmark of 1,000 image-text pairs across Punjabi's three scripts, and found that state-of-the-art vision-language models show a systematic Script Gap, with accuracy differences up to 16%…

Researchers introduced MapSatisfyBench, a benchmark to evaluate how well large language model agents in map services recover implicit user needs from available information before responding. The benchmark, built from rea…

A new study reveals that large language models exhibit a strong brand bias, recommending well-known brands 100% of the time when product specifications are identical, but this dominance can be broken by minor rating adva…

Researchers introduced SpeechDx, a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions, designed to evaluate generalization by structuring tasks according to di…

Researchers introduced MemTrace, a benchmark that evaluates LLM agents' long-term memory by probing individual knowledge points across dimensions like memory age, question type, and evidence condition. Testing 13 memory-…

Researchers propose a self-evolving framework for legal case retrieval that uses an LLM-based agent to iteratively create and refine rewriting rules for BM25, outperforming non-evolutionary baselines on the LeCaRD-v2 ben…

A developer from Gate of AI published a tutorial on fine-tuning large language models for specialized communication tasks, using homeless shelters as a case study. The tutorial demonstrates how to structure data from the…

Oxlo.ai demonstrates how few-shot learning with large language models enables domain-specific classification without weight updates, using in-context learning to infer patterns from exemplars. The company highlights its …