Dmitry Simakov

Aleksey Lapin, Igor Hromov, Stanislav Chumakov et al.

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 introduce LightAutoDS-Tab, a multi-AutoML agentic system for tasks with tabular data, which combines an LLM-based code generation with several AutoML tools. Our approach improves the flexibility and robustness of pipeline design, outperforming state-of-the-art open-source solutions on several data science tasks from Kaggle. The code of LightAutoDS-Tab is available in the open repository https://github.com/sb-ai-lab/LADS

Anton Vakhrushev, Alexander Ryzhkov, Maxim Savchenko et al.

We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of the experienced data scientists while building high-quality ML models significantly faster than these data scientists. We also compare the performance of our system with various general-purpose open source AutoML solutions and show that it performs better for most of the ecosystem and OpenML problems. We also present the lessons that we learned while developing the AutoML system and moving it into production.

Daria Grushina, Kseniia Kuvshinova, Alina Kostromina et al.

Supervised classification for tabular data remains a core machine learning task, yet its reliance on large labeled datasets limits applicability in data-scarce domains. For such few-shot scenarios, specialized methods like TabPFN - a state-of-the-art Prior-Data Fitted Network - have set a high standard by leveraging large-scale synthetic pretraining, though they still require a context of labeled examples to function. In contrast, Large Language Models (LLMs) could offer a more flexible alternative via zero- and few-shot in-context learning directly from task descriptions, but their performance on tabular data remains inconsistent and poorly understood. We introduce LLMTabBench, a benchmark designed to systematically evaluate LLMs for tabular classification under data-scarce conditions. LLMTabBench explicitly probes (i) how LLM prior knowledge interacts with in-context information (task descriptions and few-shot examples), and (ii) how model performance scales with increasing data complexity, using both real-world and controlled synthetic datasets. Our findings include: (1) LLMs are highly competitive in zero-shot settings and can outperform alternative models, even when those models have access to few-shot examples; (2) incorporating additional few-shot examples can conflict with LLM prior knowledge, limiting or even degrading performance; and (3) there is a data complexity threshold beyond which LLMs' performance declines and few-shot examples become less effective. Together, these findings reveal fundamental constraints of in-context learning for tabular data and provide practical guidance for deploying LLMs in low-data regimes.