Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25 (2025.emnlp-main)
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| Challenge: | Interpretability in information retrieval (IR) models is coarse-grained and poorly understood . a cross-encoder model extracts traditional relevance signals, such as term frequency and inverse document frequency . |
| Approach: | They analyze how a common IR model extracts traditional relevance signals . this is similar to the probabilistic ranking function BM25 . |
| Outcome: | The proposed model extracts traditional relevance signals in early-to-middle layers, similar to BM25 . the model then combine these concepts in later layers, laying the groundwork for future interventions . |
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Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
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| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
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The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval (D19-1)
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Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths (2026.findings-acl)
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Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation (2024.emnlp-main)
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| Challenge: | Currently, local explanations are not effective in predicting the model’s behavior on unseen texts. |
| Approach: | They propose a method to build a relevance thesaurus containing semantically relevant query term and document term pairs which can augment BM25 scoring functions to better approximate the neural model’s predictions. |
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Neural Retriever and Go Beyond: A Thesis Proposal (2022.naacl-srw)
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| Challenge: | Existing neural retrievers are developed for pure-text queries, which prevents them from handling multi-modality queries. |
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