TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions

• URL: http://arxiv.org/abs/2502.21024v2
• Date: Tue, 08 Apr 2025 13:11:58 GMT
• Title: TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions
• Authors: Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat, Avishek Anand, Adam Jatowt,
• Abstract summary: We propose TempRetriever, which explicitly incorporates temporal information by embedding both the query date and document timestamp into the retrieval process.<n> TempRetriever achieves a 6.63% improvement in Top-1 retrieval accuracy and a 3.79% improvement in NDCG@10 compared to the standard DPR on ArchivalQA.<n>We also propose a novel, time-based negative sampling strategy which further enhances retrieval performance by addressing temporal misalignment during training.
• Score: 18.87473448633352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal awareness is crucial in many information retrieval tasks, particularly in scenarios where the relevance of documents depends on their alignment with the query's temporal context. Traditional approaches such as BM25 and Dense Passage Retrieval (DPR) focus on lexical or semantic similarity but tend to neglect the temporal alignment between queries and documents, which is essential for time-sensitive tasks like temporal question answering (TQA). We propose TempRetriever, a novel extension of DPR that explicitly incorporates temporal information by embedding both the query date and document timestamp into the retrieval process. This allows retrieving passages that are not only contextually relevant but also aligned with the temporal intent of queries. We evaluate TempRetriever on two large-scale datasets ArchivalQA and ChroniclingAmericaQA demonstrating its superiority over baseline retrieval models across multiple metrics. TempRetriever achieves a 6.63\% improvement in Top-1 retrieval accuracy and a 3.79\% improvement in NDCG@10 compared to the standard DPR on ArchivalQA. Similarly, for ChroniclingAmericaQA, TempRetriever exhibits a 9.56\% improvement in Top-1 retrieval accuracy and a 4.68\% improvement in NDCG@10. We also propose a novel, time-based negative sampling strategy which further enhances retrieval performance by addressing temporal misalignment during training. Our results underline the importance of temporal aspects in dense retrieval systems and establish a new benchmark for time-aware passage retrieval.

Related papers

• Efficient Conversational Search via Topical Locality in Dense Retrieval [9.38751103209178]
  We exploit the topical locality inherent in conversational queries to improve response time. By leveraging query embedding similarities, we dynamically restrict the search space to semantically relevant document clusters. Our results show that the proposed system effectively handles complex, multiturn queries with high precision and efficiency.
  arXiv  Detail & Related papers  (2025-04-30T10:56:34Z)
• From Retrieval to Generation: Comparing Different Approaches [15.31883349259767]
  We evaluate retrieval-based, generation-based, and hybrid models for knowledge-intensive tasks.<n>We show that dense retrievers, particularly DPR, achieve strong performance in ODQA with a top-1 accuracy of 50.17% on NQ.<n>We also analyze language modeling tasks using WikiText-103, showing that retrieval-based approaches like BM25 achieve lower perplexity compared to generative and hybrid methods.
  arXiv  Detail & Related papers  (2025-02-27T16:29:14Z)
• Efficient Long Context Language Model Retrieval with Compression [57.09163579304332]
  Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR) We propose a new compression approach tailored for LCLM retrieval, which is trained to maximize the retrieval performance while minimizing the length of the compressed passages. We show that CoLoR improves the retrieval performance by 6% while compressing the in-context size by a factor of 1.91.
  arXiv  Detail & Related papers  (2024-12-24T07:30:55Z)
• MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering [3.117448929160824]
  temporal relations and answering time-sensitive questions is a challenging task for question-answering systems powered by large language models (LLMs)<n>We introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating temporal perturbations and gold evidence labels.<n>On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy.
  arXiv  Detail & Related papers  (2024-12-20T03:58:27Z)
• ECLIPSE: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval [14.72046677914345]
  Recent advances in Information Retrieval have leveraged high-dimensional embedding spaces to improve the retrieval of relevant documents.<n>Despite these high-dimensional representations, documents relevant to a query reside on a lower-dimensional, query-dependent manifold.<n>We propose a novel methodology that addresses these limitations by leveraging information from both relevant and non-relevant documents.
  arXiv  Detail & Related papers  (2024-12-19T15:45:06Z)
• Quam: Adaptive Retrieval through Query Affinity Modelling [15.3583908068962]
  Building relevance models to rank documents based on user information needs is a central task in information retrieval and the NLP community. We propose a unifying view of the nascent area of adaptive retrieval by proposing, Quam. Our proposed approach, Quam improves the recall performance by up to 26% over the standard re-ranking baselines.
  arXiv  Detail & Related papers  (2024-10-26T22:52:12Z)
• BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval [54.54576644403115]
  Many complex real-world queries require in-depth reasoning to identify relevant documents. We introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. Our dataset consists of 1,384 real-world queries spanning diverse domains, such as economics, psychology, mathematics, and coding.
  arXiv  Detail & Related papers  (2024-07-16T17:58:27Z)
• DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering [4.364937306005719]
  RAG has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA) We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. A two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers.
  arXiv  Detail & Related papers  (2024-06-11T15:15:33Z)
• Dense X Retrieval: What Retrieval Granularity Should We Use? [56.90827473115201]
  Often-overlooked design choice is the retrieval unit in which the corpus is indexed, e.g. document, passage, or sentence. We introduce a novel retrieval unit, proposition, for dense retrieval. Experiments reveal that indexing a corpus by fine-grained units such as propositions significantly outperforms passage-level units in retrieval tasks.
  arXiv  Detail & Related papers  (2023-12-11T18:57:35Z)
• DAPR: A Benchmark on Document-Aware Passage Retrieval [57.45793782107218]
  We propose and name this task emphDocument-Aware Passage Retrieval (DAPR) While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. Our created benchmark enables future research on developing and comparing retrieval systems for the new task.
  arXiv  Detail & Related papers  (2023-05-23T10:39:57Z)
• 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)
• Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval [25.402767809863946]
  Inverted file structure is a common technique for accelerating dense retrieval. In this work, we present the Hybrid Inverted Index (HI$2$), where the embedding clusters and salient terms work to accelerate dense retrieval.
  arXiv  Detail & Related papers  (2022-10-11T15:12:41Z)
• A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases [67.33560134350427]
  TempQA-WD is a benchmark dataset for temporal reasoning. It is based on Wikidata, which is the most frequently curated, openly available knowledge base.
  arXiv  Detail & Related papers  (2022-01-15T08:49:09Z)
• Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback [29.719150565643965]
  This paper proposes ANCE-PRF, a new query encoder that uses pseudo relevance feedback (PRF) to improve query representations for dense retrieval. ANCE-PRF uses a BERT encoder that consumes the query and the top retrieved documents from a dense retrieval model, ANCE, and it learns to produce better query embeddings directly from relevance labels. Analysis shows that the PRF encoder effectively captures the relevant and complementary information from PRF documents, while ignoring the noise with its learned attention mechanism.
  arXiv  Detail & Related papers  (2021-08-30T18:10:26Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)
• A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection [112.0514737686492]
  In this paper, we argue that by exploiting the intrinsic structure of the original rank together with an effective word-relatedness encoder, we can achieve competitive results. Our model takes 9.5 seconds to train on the WikiQA dataset, i.e., very fast in comparison with the $sim 18$ minutes required by a standard BERT-base fine-tuning.
  arXiv  Detail & Related papers  (2020-03-04T22:12:18Z)

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.