MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2412.15540v1)

Abstract: Understanding temporal relations and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by LLMs. Existing approaches either update the parametric knowledge of LLMs with new facts, which is resource-intensive and often impractical, or integrate LLMs with external knowledge retrieval (i.e., retrieval-augmented generation). However, off-the-shelf retrievers often struggle to identify relevant documents that require intensive temporal reasoning. To systematically study time-sensitive question answering, we introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating temporal perturbations and gold evidence labels. As anticipated, all existing retrieval methods struggle with these temporal reasoning-intensive questions. We further propose Modular Retrieval (MRAG), a trainless framework that includes three modules: (1) Question Processing that decomposes question into a main content and a temporal constraint; (2) Retrieval and Summarization that retrieves evidence and uses LLMs to summarize according to the main content; (3) Semantic-Temporal Hybrid Ranking that scores each evidence summarization based on both semantic and temporal relevance. On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy.

• The paper presents a modular retrieval framework that integrates temporal reasoning to enhance time-sensitive question answering.
• It decomposes queries into semantic and temporal components, achieving top-1 recall improvements of 9.3% and 11% on the TempRAGEval benchmark.
• By combining semantic and temporal scoring for evidence ranking, MRAG improves exact match and F1 scores, offering a scalable and interpretable solution.

Overview of MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering

The paper "MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering" presents an innovative approach to addressing the challenge of time-sensitive question answering in natural language processing. The authors introduce a framework that goes beyond the conventional retrieval-augmented generation (RAG) models by incorporating temporal reasoning, which is pivotal for accurately answering questions that involve time-dependent facts.

The authors highlight the limitations of existing approaches that either update the parametric knowledge of LLMs or integrate these models with off-the-shelf retrieval systems. The former is both resource-intensive and frequently ineffective at capturing recent facts, while the latter often fails to manage the temporal reasoning required for time-sensitive queries.

TempRAGEval Benchmark

To systematically evaluate time-sensitive question answering, the authors developed the TempRAGEval benchmark. This dataset builds on existing datasets like TimeQA and SituatedQA by introducing temporal perturbations and annotations of gold-standard evidence to create contrast sets. Evaluations on this benchmark clearly demonstrate that prevalent retrieval methods exhibit substantial deficits in handling queries that require nuanced temporal reasoning, resulting in degraded performance when faced with temporal perturbations.

Modular Retrieval Framework (MRAG)

The proposed MRAG framework integrates three essential modules:

1. Question Processing Module: This component decomposes a query into its main content and temporal constraint, thereby isolating temporal relevance from semantic relevance.
2. Retrieval and Summarization Module: Utilizing existing retrieval systems, this module identifies relevant documents and employs LLMs for summarizing the main content of these documents, enriching the temporal understanding.
3. Semantic-Temporal Hybrid Ranking Module: This module ranks evidence sentences by combining a semantic score derived from dense embeddings with a temporal score calculated through predefined symbolic functions.

The MRAG framework is touted for its transparency and modularity, which allows the integration of off-the-shelf technologies and offers enhancements through temporal scoring mechanisms.

Numerical Results and Implications

Evaluation results on the TempRAGEval benchmark indicate that MRAG significantly outperforms state-of-the-art retrieval systems, improving top-1 answer recall by 9.3% and top-1 evidence recall by 11%. These improvements have downstream effects, leading to enhanced question-answering accuracy with up to a 4.5% increase in exact match and F1 scores, demonstrating the framework’s robustness to temporal perturbations.

Theoretical and Practical Implications

The research demonstrates that disentangling the retrieval process into modular components, with a specific focus on temporal reasoning, can markedly enhance the performance of AI systems in time-sensitive contexts. This separation of concerns allows for more scalable and interpretable models, facilitating better diagnostics of inadequacies in system performance. Practically, MRAG can be applied to any LLM and retrieval system, allowing for improvements across various domains requiring dynamic factual updates, such as financial markets or real-time news analytics.

Future Developments

Speculating on future developments, the research paves the way for a deeper integration of symbolic reasoning with machine learning models, which can further enhance temporal reasoning capabilities. This approach also sets a precedent for developing benchmarks and systems that conscientiously address the intricacies of temporal dynamics, potentially leading to advancements in other forms of reasoning-intensive retrieval tasks.

In conclusion, the MRAG framework offers a step forward in the field of question answering, providing a robust solution to the complexities inherent in processing time-sensitive data. The modularity and adaptability of MRAG make it a powerful tool for both researchers and practitioners looking to refine AI systems' capabilities in handling temporal queries.