9 papers
Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures
Oleg Somov, Mikhail Chaichuk, Gleb Ershov +4
In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before committing to a final decision.…
Harnessing non-adversarial robustness in large language models
Qinghua Zhou, Ellina Aleshina, Andrey Lovyagin +6
The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by semantically similar but text…
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…
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…
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…
When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs
Mikhail Seleznyov, Mikhail Chaichuk, Gleb Ershov +3
Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of…