Papers with sparse retrieval
Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown potential in generating hypothetical documents for query expansion, but their efficacy is dependent on the quality of the generated documents. |
| Approach: | They propose a fusion ranking framework that uses zero-shot LLM-based query expansion to enhance sparse retrieval. |
| Outcome: | The proposed framework surpasses existing LLM-based query expansion methods on three MS MARCO-related datasets and seven low-resource datasets. |
Plot Retrieval as an Assessment of Abstract Semantic Association (2024.acl-srw)
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| Challenge: | Existing information retrieval datasets cannot capture abstract semantic associations well. |
| Approach: | They propose a task that retrieves relevant plots from the book for a query using a labeled dataset. |
| Outcome: | The proposed task can be used to evaluate the performance of IR models on the novel task Plot Retrieval. |
Sparse, Dense, and Attentional Representations for Text Retrieval (2021.tacl-1)
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| Challenge: | Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query. |
| Approach: | They propose a dual-encoder-based neural model that combines the efficiency of dual encoders with expressiveness of more costly attentional architectures. |
| Outcome: | The proposed model outperforms strong alternatives in large-scale retrieval. |
M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation (2024.findings-acl)
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| Challenge: | In this paper, we introduce a new embedding model for semantic retrieval of more than 100 working languages. |
| Approach: | They propose a new embedding model that supports multi-lingual, cross-lingual and long-document retrieval . they propose integrating relevance scores from different retrieval functionalities into the teacher signal . |
| Outcome: | The proposed model exhibits superior performance on multilingual, cross-lingual, and long-document retrieval benchmarks. |
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary (2023.acl-long)
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| Challenge: | Dense retrieval models based on text representations have proven very effective, but when applied off-the-shelf they often experience a severe drop in performance. |
| Approach: | They propose to interpret the vector representations produced by dual encoders by projecting them into the model’s vocabulary space. |
| Outcome: | The proposed model significantly improves on the BEIR benchmark and in zero-shot settings. |
On Complementarity Objectives for Hybrid Retrieval (2023.acl-long)
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| Challenge: | Existing approaches to hybrid retrieval focus on sparse models to capture “residual” features neglected in spars. |
| Approach: | They propose a new objective to capture a fuller notion of complementarity . they propose to improve the model's Ratio of Complementarity to improve RoC . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative IR benchmarks with statistical significance. |
DReSD: Dense Retrieval for Speculative Decoding (2025.findings-acl)
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| Challenge: | Speculative decoding (SD) uses an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs. |
| Approach: | They propose a draft model that proposes the next few tokens from a non-parametric datastore and uses a framework that uses approximate nearest neighbour search with contextualised token embeddings to retrieve the most semantically relevant sequences for SD. |
| Outcome: | The proposed framework achieves (on average) 87% higher acceptance rates, 65% longer accepted tokens and 19% faster generation speeds compared to sparse retrieval (REST). |