Corpus Quality Improves, Retrieval Utility Does Not: An Ablation of Preprocessing and Refinement in a Speech-to-QA Pipeline
Practitioners building training corpora with a pipeline of language models keep a preprocessing or refinement (post-process) stage when switching it on raises the corpus quality.
ProofPaper ↗
Key points
- We ablate a production pipeline that turns conference speech into question-answer pairs.
- We supply each corpus to six models at inference and measure retrieval accuracy.
- Enabling the transcript rewrite lowers retrieval accuracy by 0.061, the only stage effect that survives correction for multiple comparisons.
- On our data, the null does not come from weak corpora.
Sources (1)
- [1]Corpus Quality Improves, Retrieval Utility Does Not: An Ablation of Preprocessing and Refinement in a Speech-to-QA PipelinearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:11 AM
Practitioners building training corpora with a pipeline of language models keep a preprocessing or refinement (post-process) stage when switching it on raises the corpus quality.
We ablate a production pipeline that turns conference speech into question-answer pairs.
Extractive summary: sentences quoted from the sources.
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