Large Language Models (LLMs) exhibit remarkable generative and reasoning capabilities, yet their outputs often reflect systematic cognitive biases analogous to those observed in human judgment. This paper investigates three interrelated forms of bias: confirmation bias, position bias, and framing bias. Through a series of controlled prompting experiments, we demonstrate that LLMs tend to reinforce the premises embedded in user queries (confirmation bias), favor initial or prominent elements within a prompt (position bias), and vary their conclusions depending on the positive or negative framing of the input (framing bias). We analyze these effects across different open LLMs: Qwen, Mistral, Gemma, Olmo, and LLama. These insights can inform better prompt engineering practices, strengthen evaluation benchmarks, and support the responsible use of LLMs in education, research, and decision-making.

DSSM-like models showed good results in retrieval of short documents that semantically match the query. However, these models require large collections of click-through data that are not available in some domains. On the other hand, the recent advances in NLP demonstrated the possibility to fine-tune language models and models trained on one set of tasks to achieve a state of the art results on a multitude of other tasks or to get competitive results using much smaller training sets. Following this trend, we combined DSSM-like architecture with USE (Universal Sentence Encoder) and BERT (Bidirectional Encoder Representations from Transformers) models in order to be able to fine-tune them on a small amount of click-through data and use them for information retrieval. This approach allowed us to significantly improve our search engine for statistical data.

Sentence ordering (SO) is a key component of verbal ability. It is also crucial for automatic text generation. While numerous researchers developed various methods to automatically evaluate the informativeness of the produced contents, the evaluation of readability is usually performed manually. In contrast to that, we present a self-sufficient metric for SO assessment based on text topic-comment structure. We show that this metric has high accuracy.

The amount of available data grows every day. The data can help to make better decisions. However, with growing volume and variety it becomes increasingly more difficult to find the necessary data. Traditional search engines such as ElasticSearch or Apache Solr are primarily designed to search for text documents. Whereas a search for data has its own specifics: there is less text and more structure. Knoema's search engine is designed specifically to search for data by leveraging data's structure in order to get better results compared to document-oriented search engines.