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Summary
| Description |
English: "Figure 1: An Overview of WikiSP. An entity linker is used to link entities in the user query to their unique ID in Wikidata; e.g. “A Bronx Tale” is linked to entity ID “Q1130705”. The query and entity linker outputs are fed to the WikiSP semantic parser to produce a modified version of SPARQL, where property IDs (e.g. “P915”) are replaced by their unique string identifiers (e.g. “filming_location”). If applying the query to Wikidata fails to return a result, we default to GPT-3, labeling the result as a GPT-3 guess. Returned answers are presented in the context of the query, so the user can tell if the answer is acceptable; if not, we also show the guess from GPT-3. Here WikiSP mistakenly uses “filming_location” instead of “narrative_location”; the user detects the mistake, thumbs down the answer, and the GPT-3 answer is provided." |
| Date | |
| Source | Xu, Silei; Liu, Shicheng; Culhane, Theo; Pertseva, Elizaveta; Wu, Meng-Hsi; Semnani, Sina; Lam, Monica (December 2023). "Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata". Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. EMNLP 2023. Singapore: Association for Computational Linguistics. pp. 5778–5791. doi:10.18653/v1/2023.emnlp-main.353 |
| Author | Xu, Silei; Liu, Shicheng; Culhane, Theo; Pertseva, Elizaveta; Wu, Meng-Hsi; Semnani, Sina; Lam, Monica |
Licensing
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 20:29, 23 December 2023 | | 1,386 × 2,260 (588 KB) | HaeB | Uploaded a work by Xu, Silei; Liu, Shicheng; Culhane, Theo; Pertseva, Elizaveta; Wu, Meng-Hsi; Semnani, Sina; Lam, Monica from Xu, Silei; Liu, Shicheng; Culhane, Theo; Pertseva, Elizaveta; Wu, Meng-Hsi; Semnani, Sina; Lam, Monica (December 2023). "Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata". Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. EMNLP 2023. Singapore: Association for Computatio... |
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