6 papers
LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots
Daria Grushina, Kseniia Kuvshinova, Alina Kostromina +3
Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings. Fe…
Hallucination Detection in LLMs with Topological Divergence on Attention Graphs
Alexandra Bazarova, Andrei Volodichev, Aleksandr Yugay +10
Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models (LLMs). We introduce TOHA, a TOpology-based HAllucination detect…
Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Anton Klenitskiy, Konstantin Polev, Daria Denisova +3
Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important…
Tsururu: A Python-based Time Series Forecasting Strategies Library
Alina Kostromina, Kseniia Kuvshinova, Aleksandr Yugay +2
While current time series research focuses on developing new models, crucial questions of selecting an optimal approach for training such models are underexplored. Tsururu, a Pytho…
LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
Aleksey Lapin, Igor Hromov, Stanislav Chumakov +4
AutoML has advanced in handling complex tasks using the integration of LLMs, yet its efficiency remains limited by dependence on specific underlying tools. In this paper, we introd…
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs
Julia Belikova, Konstantin Polev, Rauf Parchiev +1
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly deployed in industry applications, yet their reliability remains hampered by challeng…