Data Selection for Multi-turn Dialogue Instruction Tuning
Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsistent, with topic drift, repetitive chitchat, and mismatched answer formats across turns. We address this from a data selection perspective and propose \textbf{MDS} (Multi-turn Dialogue Selection), a dialogue-level framework that scores whole conversations rather than isolated turns. MDS combines a global coverage stage that performs bin-wise selection in the user-query trajectory space to retain representative yet non-redundant dialogues, with a local structural stage that evaluates within-dialogue reliability through entity-grounded topic g...
| Authors | Bo Li, Shikun Zhang, Wei Ye |
|---|---|
| Primary category | cs.CL |
| Published | 9 April 2026 |
| Last updated at source | 19 April 2026 |
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