Research map / Large language models / Retrieval and knowledge
Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| correct the 22 Geoquery gold queries that evaluate incorrectly | Our System, 10-fold · Geoquery | answer accuracy (%): 86.3 -> 88.8 | Helped |
| correct the 22 Geoquery gold queries that evaluate incorrectly | Our System, 2-fold · Geoquery | answer accuracy (%): 84.8 -> 87.4 | Helped |
| reduce cross-validation from 10-fold to 2-fold (far fewer training examples) | Our System · Geoquery corrected | answer accuracy (%): 88.8 -> 87.4 | Hurt |
| reduce training set to 10 QA pairs | Our System · Geoquery corrected | answer accuracy (%): 88.8 -> 51.6 | Hurt |
| use only 100 QA pairs for training instead of full 10-fold training | Our System · Geoquery corrected | answer accuracy (%): 88.8 -> 85.6 | No clear effect |
| compare unannotated-QA training against annotated logical-form training (KZGS10) | Our System corrected 10-fold · Geoquery | answer accuracy (%): 88.9 -> 88.8 | No clear effect |
| LJK11 manually augmented triggers versus base triggers | LJK11, 680/200 · Geoquery | answer accuracy (%): 87.9 -> 91.4 | Helped |
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