10 papers
Compact Path Representation in DAGs via Colored Edge Pebbling
Paola Bonizzoni, Alessio Conte, Gianluca Della Vedova +3
Compactly representing a variation graph is a core problem in computational pangenomics that is usually attacked with techniques that have been originated on texts and adapted to g…
Boosting Self-Consistency with Ranking
Maria Marina, Daniil Moskovskiy, Sergey Pletenev +3
Self-consistency improves large language models by sampling multiple reasoning paths and selecting the most frequent answer, but majority voting often fails to recover correct answ…
Tracing Persona Vectors Through LLM Pretraining
Viktor Moskvoretskii, Dominik Glandorf, Jorge Medina Moreira +2
How large language models internally represent high-level behaviors is a core interpretability question with direct relevance to AI safety: it determines what we can detect, audit,…
Evolutionary Search for Automated Design of Uncertainty Quantification Methods
Mikhail Seleznyov, Daniil Korbut, Viktor Moskvoretskii +3
Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and genera…
Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval
Artem Vazhentsev, Maria Marina, Daniil Moskovskiy +8
Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs). To enhance trust, natural language claims from diverse sources, including…
Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA
Sergey Pletenev, Maria Marina, Nikolay Ivanov +6
Large Language Models (LLMs) often hallucinate in question answering (QA) tasks. A key yet underexplored factor contributing to this is the temporality of questions -- whether they…