Papers by Adithya Bhaskar

2 papers
Benchmarking and Improving Text-to-SQL Generation under Ambiguity (2023.emnlp-main)

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Challenge: Existing decoding algorithms treat SQL queries as a string and produce unhelpful token-level diversity in the top-k.
Approach: They propose a benchmarking algorithm that generates all SQLs in top-k ranked outputs . they use plan-based template generation and constrained infilling to bridge this gap .
Outcome: The proposed algorithm is 2.5 times more effective than state-of-the-art models at generating all candidate SQLs in the top-k ranked outputs.
Prompted Opinion Summarization with GPT-3.5 (2023.findings-acl)

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Challenge: Recent years have seen several shifts in summarization research, including extractive models.
Approach: They propose a pipeline method for applying GPT-3.5 to summarize user reviews . they propose three new metrics targeting faithfulness, factuality, and genericity .
Outcome: The proposed methods perform well in opinion summarization, the authors show . they also show that standard evaluation metrics do not reflect this performance .

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