SOURCE-LINKED INTELLIGENCE
To Consolidate or not to Consolidate? Evaluating the Impact of Consolidation in Multi-Reference Training using Peer Reviews
Natural language generation (NLG) tasks span the spectrum of conditional entropy, ranging from highly constrained machine translation to open-ended dialogue generation. Structured tasks like automated peer-review generation occupy the intermediate region, where a single input admits multiple valid, overlapping outputs. In this work, we demonstrate that traditional single- and multi-reference training paradigms are suboptimal for these intermediary tasks. We provide empirical evidence that consolidating diverse references into a unified training signal is crucial for developing effective system
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-19T06:13:28.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.