1101 papers
This paper introduces a maximum likelihood model to estimate LLM usage in AI conference peer reviews, revealing that between 6.5% and 16.9% of text in recent reviews was substantially AI-generated, with higher usage correlated to lower reviewer confidence, proximity to deadlines, and lower engagement with rebuttals.
This paper demonstrates that restricting non-blocking broadcast protocols to the Wait-Only property reduces the computational complexity of state and configuration coverability problems from Ackermann-hard to P-complete and PSPACE-complete, respectively.
This paper proposes a lightweight, topic-guided watermarking scheme for large language models that partitions the vocabulary into topic-aligned subsets to embed robust, detectable signatures while preserving text quality and requiring no additional framework overhead.
This large-scale study of over 100 million Reddit comments reveals that trigger words in UK political discussions significantly increase user engagement while fostering more negative, angry, and hateful exchanges, thereby highlighting their role in driving online polarization.
This paper introduces NutriBench, the first publicly available benchmark of 11,857 human-verified meal descriptions with macro-nutrient labels, to evaluate the performance and real-world health implications of leading Large Language Models in estimating nutrition from text.
This paper introduces MELODI, a framework and accompanying dataset designed to monitor and analyze the energy consumption of large language model inference, revealing significant disparities in efficiency based on prompt attributes and highlighting the need for sustainable optimization strategies.
This paper introduces BA-LoRA, a novel parameter-efficient fine-tuning method that mitigates "Catastrophic Inheritance" in Large Language Models by employing targeted regularizers to address knowledge drift, representation collapse, and noise overfitting, thereby enhancing model robustness, fairness, and performance across diverse NLU and NLG tasks.
This paper proposes and evaluates the OM4OV pipeline, which leverages ontology matching systems for ontology versioning while introducing a cross-reference optimization mechanism to address performance limitations and improve the detection of update entities.
This paper challenges the assumption that annotator disagreements in preference datasets are mere noise by categorizing their diverse sources, demonstrating how standard reward modeling and evaluation methods fail to account for these divergences, and proposing new techniques to identify and mitigate their impact on LLM training and assessment.
The StarWhisper Telescope system is an AI agent framework that automates end-to-end astronomical observations by integrating large language models with specialized workflows to autonomously plan observations, analyze data, and trigger follow-ups, thereby reducing human intervention and demonstrating scalable potential for future large-scale telescope arrays.
This paper empirically validates the theoretical relationship between hallucination, monofact rates, and miscalibration in language models and demonstrates that selectively upweighting a small fraction of training data can significantly reduce hallucinations without compromising accuracy, thereby challenging the necessity of universal deduplication policies.
This paper introduces \texttt{SEM-CTRL}, a unified decoding framework that integrates token-level Monte Carlo Tree Search guided by Answer Set Grammars to enforce syntactic and semantic constraints on off-the-shelf LLMs, enabling even small models to outperform larger reasoning models while guaranteeing valid, context-aware outputs without fine-tuning.
BioChemInsight is an open-source pipeline that integrates advanced optical recognition and large language models to automatically extract chemical structures and bioactivity data from patents with over 90% accuracy, thereby significantly accelerating drug discovery by unlocking complementary chemical space not found in public databases like ChEMBL.
This paper introduces a novel surrogate model that simultaneously preserves both the empirical symbol frequency distributions (such as Zipf's law) and the long-range correlation structures of symbolic sequences like language and DNA by mapping fractional Gaussian noise onto the original histogram, thereby enabling the disentanglement of structural features and the testing of scaling law origins.
This paper introduces FeynTune, a suite of 20 specialized Large Language Models fine-tuned on High-Energy Physics arXiv abstracts that outperform both their base model and leading commercial LLMs in theoretical physics tasks, offering valuable insights for developing domain-specific AI in the field.
This study reveals that while ChatGPT temporarily boosts human creative performance, its use ultimately leads to a reversion to baseline creativity and a persistent homogenization of content, challenging the notion that generative AI enhances long-term human creativity.
This paper addresses the safety challenges of end-to-end conversational AI by surveying the problem landscape, proposing a value-sensitive design framework for release decisions, and providing a suite of tools to help researchers mitigate potential harms.
The paper introduces BERT, a novel bidirectional language representation model that leverages pre-training on unlabeled text to achieve state-of-the-art performance across a wide range of natural language processing tasks with minimal fine-tuning.
This paper introduces the Transformer, a novel neural network architecture that relies entirely on attention mechanisms while eliminating recurrence and convolutions, demonstrating superior translation quality, faster training times, and strong generalization to other tasks compared to existing state-of-the-art models.
This paper introduces two novel, computationally efficient model architectures for learning high-quality continuous word vector representations from massive datasets, which achieve state-of-the-art performance in measuring syntactic and semantic word similarities at a fraction of the previous computational cost.