UDC 004.896
ANALYSIS OF DECODING STRATEGIES FOR LARGE LANGUAGE
MODEL RESPONSES IN MATHEMATICAL CONSULTING PROBLEMS
WITH LIMITED COMPUTATIONAL RESOURCES
F. I. Bashkin, post-graduate student, RSREU, Ryazan, Russia;
orcid.org/0009-0001-9574-8761, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
S. V. Skvortsov, Dr. in technical sciences, full professor, RSREU, Ryazan, Russia;
orcid.org/0000-0001-9495-4953, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
The problem of improving the quality of text generation by large language models in the conditions of
limited computational resources is considered. The aim of the work is to develop and evaluate the efficiency
of iterative quality control system implementing a set of decoding strategies. Due to high computational
complexity of directly obtaining optimal responses, an architecture that integrates direct decoding, iterative
improvement with neural network evaluation, document-oriented decoding, and a combined approach is
proposed. To aggregate quality metrics, a mathematical apparatus including BLEU, ROUGE, cosine similarity,
and perplexity is used. An experimental study of the influence of various strategies on the quality of
generated responses is carried out. The authors show that the proposed system provides a significant increase
in accuracy and completeness of responses during autonomous operation on local resources, demonstrating
an advantage due to the possibility of additional training on specialized corpora and maintaining
data confidentiality, which is promising for deployment in secure educational environments.
Key words: large language models (LLM), decoding strategies, text quality metrics, autonomous systems,
neural networks, educational technologies, machine learning