Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computational costs. Besides, RAG is not always needed as may introduce irrelevant information. Recent adaptive retrieval methods integrate LLMs’ intrinsic knowledge with external information appealing to LLM self-knowledge, but they often neglect efficiency evaluations and comparisons with uncertainty estimation techniques. We bridge this gap by conducting a comprehensive analysis of 35 adaptive retrieval methods, including 8 recent approaches and 27 uncertainty estimation techniques, across 6 datasets using 10 metrics for QA performance, self-knowledge, and efficiency. Our findings show that uncertainty estimation techniques often outperform complex pipelines in terms of efficiency and self-knowledge, while maintaining comparable QA performance.

The task of persuasion techniques detection is limited by several challenges, such as insufficient training data and ambiguity in labels. In this paper, we describe a solution for the Slavic NLP 2025 Shared Task. It utilizes multilingual XLM-RoBERTa, that was trained on 100 various languages, and Slavic BERT, a model fine-tuned on four languages of the Slavic group. We suggest to augment the training dataset with related data from previous shared tasks, as well as some automatic translations from English and German. The resulting solutions are ranked among the top 3 for Russian in the Subtask 1 and for all languages in the Subtask 2. We release the code for our solution - https://github.com/ssenichev/ACL_SlavicNLP2025.

In this work, we propose an efficient answer retrieval system **EARS**: a production-ready, factual question answering (QA) system that combines local knowledge base search with generative, context-based QA. To assess the quality of the generated content, we devise comprehensive metrics for both manual and automatic evaluation of the answers to questions. A distinctive feature of our system is the Ranker component, which ranks answer candidates based on their relevance. This feature enhances the effectiveness of local knowledge base retrieval by 23%. Another crucial aspect of our system is the LLM, which utilizes contextual information from a web search API to generate responses. This results in substantial 92.8% boost in the usefulness of voice-based responses. **EARS** is language-agnostic and can be applied to any data domain.