2 citations · 6 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
Maria Marina, Nikolay Ivanov, Sergey Pletenev +6
Large Language Models~(LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high computational cost while risking misinformatio…
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
Sergey Pletenev, Maria Marina, Daniil Moskovskiy +4
The performance of Large Language Models (LLMs) on many tasks is greatly limited by the knowledge learned during pre-training and stored in the model's parameters. Low-rank adaptat…
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
Viktor Moskvoretskii, Maria Lysyuk, Mikhail Salnikov +7
Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computationa…
Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering
Maria Lysyuk, Mikhail Salnikov, Pavel Braslavski +1
While being one of the most popular question types, simple questions such as "Who is the author of Cinderella?", are still not completely solved. Surprisingly, even the most powerf…
Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski +3
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the model…
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov, Hai Le, Prateek Rajput +4
Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks. In this paper,…