Papers › Variational Open-Domain Question Answering
Variational Open-Domain Question Answering
Valentin Liévin, Andreas Geert Motzfeldt, Ida Riis Jensen, Ole Winther
Retrieval-augmented models have proven to be effective in natural language processing tasks, yet there remains a lack of research on their optimization using variational inference. We introduce the Variational Open-Domain (VOD) framework for end-to-end training and evaluation of retrieval-augmented models, focusing on open-domain question answering and language modelling. The VOD objective, a self-normalized estimate of the R\'enyi variational bound, approximates the task marginal likelihood and is evaluated under samples drawn from an auxiliary sampling distribution (cached retriever and/or approximate posterior). It remains tractable, even for retriever distributions defined on large corpora. We demonstrate VOD's versatility by training reader-retriever BERT-sized models on multiple-choice medical exam questions. On the MedMCQA dataset, we outperform the domain-tuned Med-PaLM by +5.3% despite using 2.500× fewer parameters. Our retrieval-augmented BioLinkBERT model scored 62.9% on the MedMCQA and 55.0% on the MedQA-USMLE. Last, we show the effectiveness of our learned retriever component in the context of medical semantic search.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multiple Choice Question Answering (MCQA) | MedMCQA | VOD (BioLinkBERT) | Dev Set (Acc-%) | 0.583 | #4 of 22 | Archive leaderboard | report |
| Multiple Choice Question Answering (MCQA) | MedMCQA | VOD (BioLinkBERT) | Test Set (Acc-%) | 0.629 | #4 of 22 | Archive leaderboard | report |
| Question Answering | MedQA | VOD (BioLinkBERT) | Accuracy | 55.0 | #15 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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