End-to-End Training of Multi-Document Reader and Retriever for\n Open-Domain Question Answering
Bibliographic record
Abstract
We present an end-to-end differentiable training method for\nretrieval-augmented open-domain question answering systems that combine\ninformation from multiple retrieved documents when generating answers. We model\nretrieval decisions as latent variables over sets of relevant documents. Since\nmarginalizing over sets of retrieved documents is computationally hard, we\napproximate this using an expectation-maximization algorithm. We iteratively\nestimate the value of our latent variable (the set of relevant documents for a\ngiven question) and then use this estimate to update the retriever and reader\nparameters. We hypothesize that such end-to-end training allows training\nsignals to flow to the reader and then to the retriever better than staged-wise\ntraining. This results in a retriever that is able to select more relevant\ndocuments for a question and a reader that is trained on more accurate\ndocuments to generate an answer. Experiments on three benchmark datasets\ndemonstrate that our proposed method outperforms all existing approaches of\ncomparable size by 2-3% absolute exact match points, achieving new\nstate-of-the-art results. Our results also demonstrate the feasibility of\nlearning to retrieve to improve answer generation without explicit supervision\nof retrieval decisions.\n
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".