Wikipedia-based Datasets in Russian Information Retrieval Benchmark RusBEIR

• URL: http://arxiv.org/abs/2511.05079v1
• Date: Fri, 07 Nov 2025 08:53:34 GMT
• Title: Wikipedia-based Datasets in Russian Information Retrieval Benchmark RusBEIR
• Authors: Grigory Kovalev, Natalia Loukachevitch, Mikhail Tikhomirov, Olga Babina, Pavel Mamaev,
• Abstract summary: We present a novel series of Russian information retrieval datasets constructed from the "Did you know..." section of Russian Wikipedia.<n>Our datasets support a range of retrieval tasks, including fact-checking, retrieval-augmented generation, and full-document retrieval.
• Score: 0.4893345190925178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a novel series of Russian information retrieval datasets constructed from the "Did you know..." section of Russian Wikipedia. Our datasets support a range of retrieval tasks, including fact-checking, retrieval-augmented generation, and full-document retrieval, by leveraging interesting facts and their referenced Wikipedia articles annotated at the sentence level with graded relevance. We describe the methodology for dataset creation that enables the expansion of existing Russian Information Retrieval (IR) resources. Through extensive experiments, we extend the RusBEIR research by comparing lexical retrieval models, such as BM25, with state-of-the-art neural architectures fine-tuned for Russian, as well as multilingual models. Results of our experiments show that lexical methods tend to outperform neural models on full-document retrieval, while neural approaches better capture lexical semantics in shorter texts, such as in fact-checking or fine-grained retrieval. Using our newly created datasets, we also analyze the impact of document length on retrieval performance and demonstrate that combining retrieval with neural reranking consistently improves results. Our contribution expands the resources available for Russian information retrieval research and highlights the importance of accurate evaluation of retrieval models to achieve optimal performance. All datasets are publicly available at HuggingFace. To facilitate reproducibility and future research, we also release the full implementation on GitHub.

Related papers

• MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs [48.73595915402094]
  MOLE is a framework that automatically extracts metadata attributes from scientific papers covering datasets of languages other than Arabic.<n>Our methodology processes entire documents across multiple input formats and incorporates robust validation mechanisms for consistent output.
  arXiv  Detail & Related papers  (2025-05-26T10:31:26Z)
• Interpreting Multilingual and Document-Length Sensitive Relevance Computations in Neural Retrieval Models through Axiomatic Causal Interventions [0.0]
  This study analyzes and extends the paper "Axiomatic Causal Interventions for Reverse Engineering Relevance in Neural Retrieval Models"<n>We reproduce key experiments from the original paper, confirming that information on query terms is captured in the model encoding.<n>We extend this work by applying activation patching to Spanish and Chinese datasets and by exploring whether document-length information is encoded in the model as well.
  arXiv  Detail & Related papers  (2025-05-04T15:30:45Z)
• Building Russian Benchmark for Evaluation of Information Retrieval Models [0.0]
  RusBEIR is a benchmark for evaluation of information retrieval models in the Russian language.<n>It integrates adapted, translated, and newly created datasets, enabling comparison of lexical and neural models.
  arXiv  Detail & Related papers  (2025-04-17T12:11:14Z)
• GeAR: Generation Augmented Retrieval [82.20696567697016]
  This paper introduces a novel method, $textbfGe$neration.<n>It improves the global document-Query similarity through contrastive learning, but also integrates well-designed fusion and decoding modules.<n>When used as a retriever, GeAR does not incur any additional computational cost over bi-encoders.
  arXiv  Detail & Related papers  (2025-01-06T05:29:00Z)
• RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling [79.56442336234221]
  We introduce RegaVAE, a retrieval-augmented language model built upon the variational auto-encoder (VAE) It encodes the text corpus into a latent space, capturing current and future information from both source and target text. Experimental results on various datasets demonstrate significant improvements in text generation quality and hallucination removal.
  arXiv  Detail & Related papers  (2023-10-16T16:42:01Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks [62.22920673080208]
  Single-step generative model can dramatically simplify the search process and be optimized in end-to-end manner. We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.
  arXiv  Detail & Related papers  (2022-08-16T10:22:49Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval [12.514666775853598]
  We propose a novel framework to leverage the advantages of interactive and non-interactive models. We introduce semi-interactive mechanism, which builds our model upon non-interactive architecture but encodes each document together with its associated multilingual queries. Our methods significantly boost the retrieval accuracy while maintaining the computational efficiency.
  arXiv  Detail & Related papers  (2021-11-03T03:03:19Z)
• Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks [21.379555672973975]
  This paper proposes a graph neural network (GNN)-based extractive summarization model. Our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model achieves substantially state-of-the-art results on CNN/DM and NYT datasets.
  arXiv  Detail & Related papers  (2020-10-13T09:30:04Z)
• Learning from Context or Names? An Empirical Study on Neural Relation Extraction [112.06614505580501]
  We study the effect of two main information sources in text: textual context and entity mentions (names) We propose an entity-masked contrastive pre-training framework for relation extraction (RE) Our framework can improve the effectiveness and robustness of neural models in different RE scenarios.
  arXiv  Detail & Related papers  (2020-10-05T11:21:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.