Two-Step Question Retrieval for Open-Domain QA
Bibliographic record
Abstract
The retriever-reader pipeline has shown promising performance in open-domain QA but suffers from a very slow inference speed.Recently proposed question retrieval models tackle this problem by indexing question-answer pairs and searching for similar questions.These models have shown a significant increase in inference speed, but at the cost of lower QA performance compared to the retriever-reader models.This paper proposes a two-step question retrieval model, SQuID (Sequential Question-Indexed Dense retrieval) and distant supervision for training.SQuID uses two bi-encoders for question retrieval.The first-step retriever selects top-k similar questions, and the secondstep retriever finds the most similar question from the top-k questions.We evaluate the performance and the computational efficiency of SQuID.The results show that SQuID significantly increases the performance of existing question retrieval models with a negligible loss on inference speed. 1 * These authors contributed equally. 1 The implementation of SQuID has been released at https://github.com/yeonsw/SQuID.git
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".