The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
Open accessAbstract
Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens.When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable.We propose PICARD 1 , a method for constraining auto-regressive decoders of language models through incremental parsing.PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step.On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into stateof-the-art solutions.
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Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034
Adafactor: Adaptive Learning Rates with Sublinear Memory CostPreprint
Shazeer, N., & Stern, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04235 · Cited by 572
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Wang, B., Shin, R., Liu, X., Polozov, O., & Richardson, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.677 · Cited by 512
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Yin, P., & Neubig, G. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/d18-2002 · Cited by 203
Shaw, P. M., Chang, M.-W., Pasupat, P., & Toutanova, K. (2021) · doi:10.18653/v1/2021.acl-long.75 · Cited by 147
Parsec: direct style monadic parser combinators for the real world
Leijen, D., & Meijer, E. (2001) · Cited by 146
Choi, D., Shin, M. C., Kim, E., & Shin, D. R. (2021) · Computational Linguistics · doi:10.1162/coli_a_00403 · Cited by 134
Semantic Evaluation for Text-to-SQL with Distilled Test Suites
Zhong, R., Yu, T., & Klein, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.29 · Cited by 112
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
Rubin, O., & Berant, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.29 · Cited by 111
Yu, T., Zhang, R., Er, H. Y., Li, S., Xue, E., Pang, B., Lin, X., Tan, Y. C., Shi, T., Li, Z., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05378 · Cited by 102
Cited by
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., & Zhou, J. (2023) · arXiv · doi:10.48550/arxiv.2308.15363 · Cited by 485
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., & Zhou, J. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3641204.3641221 · Cited by 342
Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Cao, R., Geng, R., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.03111 · Cited by 278
Prompting Is Programming: A Query Language for Large Language Models
Beurer-Kellner, L., Fischer, M., & Vechev, M. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3591300 · Cited by 117
Teaching Large Language Models to Self-Debug
Chen, X., Lin, M., Schärli, N., & Zhou, D. (2023) · arXiv · doi:10.48550/arxiv.2304.05128 · Cited by 812
“While models designed and trained for specialized domains have achieved impressive performance in various applications such as text-to-code generation (Li et al., 2023a; Wang et al., 2020; Scholak et al., 2021; Dong & Lapata, 2016; Iyer et al., 2018) and code translation (Chen et al.”
Method
Metadata
- Authors
- Scholak, T., Schucher, N., & Bahdanau, D.
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 9895-9901
- OpenAlex
- W3200285169
- Cited by
- 323
- References
- 15
- FWCI
- 20.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Scholak, T., Schucher, N., & Bahdanau, D. (2021). PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2021.emnlp-main.779
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