Papers › SentenceMIM: A Latent Variable Language Model
SentenceMIM: A Latent Variable Language Model
Micha Livne, Kevin Swersky, David J. Fleet
SentenceMIM is a probabilistic auto-encoder for language data, trained with Mutual Information Machine (MIM) learning to provide a fixed length representation of variable length language observations (i.e., similar to VAE). Previous attempts to learn VAEs for language data faced challenges due to posterior collapse. MIM learning encourages high mutual information between observations and latent variables, and is robust against posterior collapse. As such, it learns informative representations whose dimension can be an order of magnitude higher than existing language VAEs. Importantly, the SentenceMIM loss has no hyper-parameters, simplifying optimization. We compare sentenceMIM with VAE, and AE on multiple datasets. SentenceMIM yields excellent reconstruction, comparable to AEs, with a rich structured latent space, comparable to VAEs. The structured latent representation is demonstrated with interpolation between sentences of different lengths. We demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering and transfer learning, without fine-tuning, outperforming VAE and AE with similar architectures.
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Code
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Code Syntology ran Syntology
9 samples harvested; 1 ran; 0 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | YahooCQA | sMIM (1024) + | MRR | 0.863 | #1 of 7 | Archive leaderboard | report |
| Question Answering | YahooCQA | sMIM (1024) + | P@1 | 0.757 | #1 of 7 | Archive leaderboard | report |
| Question Answering | YahooCQA | sMIM (1024) | MRR | 0.818 | #2 of 7 | Archive leaderboard | report |
| Question Answering | YahooCQA | sMIM (1024) | P@1 | 0.683 | #2 of 7 | 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.
Methods
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