TurnWise: The Gap between Single- and Multi-turn Language Model Capabilities
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914402517647360 |
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| author | Graf, Victoria Pyatkin, Valentina Dziri, Nouha Lambert, Nathan Hajishirzi, Hannaneh |
| author_facet | Graf, Victoria Pyatkin, Valentina Dziri, Nouha Lambert, Nathan Hajishirzi, Hannaneh |
| contents | Multi-turn conversations are a common and critical mode of language model interaction. However, current open training and evaluation data focus on single-turn settings, failing to capture the additional dimension of these longer interactions. To understand this multi-/single-turn gap, we first introduce a new benchmark, TurnWiseEval, for multi-turn capabilities that is directly comparable to single-turn chat evaluation. Our evaluation isolates multi-turn specific conversational ability through pairwise comparison to equivalent single-turn settings. We additionally introduce our synthetic multi-turn data pipeline TurnWiseData which allows the scalable generation of multi-turn training data. Our experiments with Olmo 3 show that training with multi-turn data is vital to achieving strong multi-turn chat performance, and that including as little as 10k multi-turn conversations during post-training can lead to a 12% improvement on TurnWiseEval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16759 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TurnWise: The Gap between Single- and Multi-turn Language Model Capabilities Graf, Victoria Pyatkin, Valentina Dziri, Nouha Lambert, Nathan Hajishirzi, Hannaneh Computation and Language Artificial Intelligence Multi-turn conversations are a common and critical mode of language model interaction. However, current open training and evaluation data focus on single-turn settings, failing to capture the additional dimension of these longer interactions. To understand this multi-/single-turn gap, we first introduce a new benchmark, TurnWiseEval, for multi-turn capabilities that is directly comparable to single-turn chat evaluation. Our evaluation isolates multi-turn specific conversational ability through pairwise comparison to equivalent single-turn settings. We additionally introduce our synthetic multi-turn data pipeline TurnWiseData which allows the scalable generation of multi-turn training data. Our experiments with Olmo 3 show that training with multi-turn data is vital to achieving strong multi-turn chat performance, and that including as little as 10k multi-turn conversations during post-training can lead to a 12% improvement on TurnWiseEval. |
| title | TurnWise: The Gap between Single- and Multi-turn Language Model Capabilities |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16759 |