2 citations · 7 across the 11 of their papers we have counts for
12 papers
Specification-Aware Predictive Uncertainty Decomposition
Roman Vashurin, Mikhail Seleznyov, Assel Yermekova +7
Classical decompositions of predictive uncertainty, such as the information-theoretic one, are defined for a fixed representation of the input object. They do not consider that…
The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning
Anna Borisiuk, Andrey Savchenko, Alexander Panchenko +1
Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless…
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…
The Chronicles of RiDiC: Generating Datasets with Controlled Popularity Distribution for Long-form Factuality Evaluation
Pavel Braslavski, Dmitrii Iarosh, Nikita Sushko +4
We present a configurable pipeline for generating multilingual sets of entities with specified characteristics, such as domain, geographical location and popularity, using data fro…
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…
CoRoVA: Compressed Representations for Vector-Augmented Code Completion
Daria Cherniuk, Nikita Sukhorukov, Danil Gusak +4
Retrieval-augmented generation has emerged as one of the most effective approaches for code completion enhancement, especially when repository-level context is important. However,…
Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMs
Nikita Afonin, Nikita Andriianov, Vahagn Hovhannisyan +9
Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these findings were limited to f…
BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
Andrey Sakhovskiy, Elena Tutubalina
In recent years, there has been substantial progress in using pretrained Language Models (LMs) on a range of tasks aimed at improving the understanding of biomedical texts. Nonethe…
The benefits of query-based KGQA systems for complex and temporal questions in LLM era
Artem Alekseev, Mikhail Chaichuk, Miron Butko +3
Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions. Query-based knowledge graph QA (KGQA) offers a modular al…
Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models
Mikhail Salnikov, Dmitrii Korzh, Ivan Lazichny +7
This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspec…
SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators
Daniil Moskovskiy, Nikita Sushko, Sergey Pletenev +2
Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. In this work, we introduce a pipeline for the generation of…
RuCCoD: Towards Automated ICD Coding in Russian
Aleksandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov +5
This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which inclu…