4 papers
ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
Vladislav Smirnov, Chieu Nguyen, Sergey Senichev +14
Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.g., via m…
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…
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…