Coherence-Aware Distributional Evaluation of Open-Ended Text Generation
Existing open-ended generation metrics measure likelihood, lexical diversity, or distributional similarity in generic representation space, yet can miss fundamental dimensions of quality. A prominent blind spot is global coherence: a generated passage may be locally fluent while remaining globally contradictory, causally inconsistent, or topically disconnected. We identify representation as a central bottleneck in detecting these failures and introduce CHORD (Coherence-aware Hidden-state Open-generation Reference Distance), a coherence-sensitive distributional metric. CHORD encodes generated and human-written corpora in the hidden-state space of a frozen LLM using a coherence-eliciting pr...
| Authors | Jinnuo Liu, Junhao Zhu, Weifeng Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 September 2026 |
| Last updated at source | 29 September 2026 |
Connected in the knowledge graph
- mikezhu/chord-distill-dataDataset · cited by
- mikezhu/chord-experiments-dataDataset · cited by
Models and datasets referencing this paper
- mikezhu/chord-experiments-dataDataset
- mikezhu/chord-distill-dataDataset
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