REVIEW 1 cited by
BM25 Query Augmentation Learned End-to-End
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
Signed reviews
read the original abstract
Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation. We propose an approach to learning an augmentation and a re-weighting end-to-end, and we find that our approach improves performance over BM25 while retaining its speed. We furthermore find that the learned augmentations and re-weightings transfer well to unseen datasets.
Forward citations
Cited by 1 Pith paper
-
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain
On a Schiphol flight-information test set, knowledge-graph RAG (91.49%) beat SQL RAG (80.85%) and traditional RAG (84.84%) on accuracy and did far better on reasoning questions (68.75% vs 6.25% and 9.38%).
Discussion (0). Continue with ORCID to comment.