Product-of-Experts Training Reduces Dataset Artifacts in Natural Language Inference
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911611296415744 |
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| author | Mathew, Aby Mammen |
| author_facet | Mathew, Aby Mammen |
| contents | Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlations, and 38.6% of the baseline errors are the result of these artifacts. We propose Product-of-Experts (PoE) training, which downweights examples where biased models are overconfident. PoE nearly preserves accuracy (89.10% vs. 89.30%) while cutting bias reliance by 4.71% (bias agreement 49.85% to 45%). An ablation finds lambda = 1.5 that best balances debiasing and accuracy. Behavioral tests still reveal issues with negation and numerical reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19069 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Product-of-Experts Training Reduces Dataset Artifacts in Natural Language Inference Mathew, Aby Mammen Computation and Language Artificial Intelligence 68T50, 68T07 I.2.7; I.2.6 Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlations, and 38.6% of the baseline errors are the result of these artifacts. We propose Product-of-Experts (PoE) training, which downweights examples where biased models are overconfident. PoE nearly preserves accuracy (89.10% vs. 89.30%) while cutting bias reliance by 4.71% (bias agreement 49.85% to 45%). An ablation finds lambda = 1.5 that best balances debiasing and accuracy. Behavioral tests still reveal issues with negation and numerical reasoning. |
| title | Product-of-Experts Training Reduces Dataset Artifacts in Natural Language Inference |
| topic | Computation and Language Artificial Intelligence 68T50, 68T07 I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2604.19069 |