One Reference or Many? Rethinking Multi-Reference Training for Intermediate-Entropy Text Generation
Natural language generation (NLG) ranges from highly constrained tasks, such as machine translation, to open-ended tasks like dialogue generation. Between them lie peer-review generation and image captioning, where one input admits several valid, overlapping outputs. We measure this intermediary region with Von Neumann entropy ($\hat{H}\approx0.6$ vs. 0.15 translation, 0.92 dialogue). Single-reference training discards valid perspectives, while multi-reference training introduces high-variance, unaligned targets. To tackle this, we propose target-side consolidation, that is, merging all references for an input into a single target which preserves every distinct information unit. To this e...
| Authors | Maitreya Prafulla Chitale, Ketaki Mangesh Shetye, Yash More et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 September 2026 |
| Last updated at source | 15 October 2026 |
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