REVIEW 5 cited by
MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Understanding temporal relations and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by large language models (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.
Forward citations
Cited by 5 Pith papers
-
Right Knowledge, Wrong Answer: Characterizing Parametric Temporal Conflict in Open-Weight Language Models
Temporal Attractor Steering resolves 29-57% of parametric temporal conflicts in open-weight LLMs while preserving 85-99% accuracy on non-conflict queries.
-
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
A systematic empirical study finds that for multimodal RAG, EVA-CLIP retrieval, listwise LVLM reranking, and feeding only the top-ranked document works best, with a self-reflection agent adding further gains.
-
DailyQA: A Benchmark to Evaluate Web Retrieval Augmented LLMs Based on Capturing Real-World Changes
DailyQA, automatically built from Wikipedia revision logs, shows that LLMs with web retrieval answer only about half of time-sensitive questions correctly, and that reranking documents outperforms both snippets and ti...
-
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective
Diffusion-language-model embeddings beat LLM embeddings on long-document and reasoning retrieval benchmarks, but the paper overstates some gains and releases no code or data.
-
Reading Between the Timelines: RAG for Answering Diachronic Questions
TA-RAG uses LLM-extracted time intervals, time-filtered retrieval with averaged temporal query embeddings, and chronologically ordered context to beat standard RAG by 13-27 points on the new ADQAB benchmark of 525 mul...
Discussion (0). Continue with ORCID to comment.