End-to-End Training of Multi-Document Reader and Retriever for\n Open-Domain Question Answering
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it