Papers by Siyu Huo
FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows (2025.emnlp-industry)
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Evelyn Duesterwald, Siyu Huo, Vatche Isahagian, K. R. Jayaram, Ritesh Kumar, Vinod Muthusamy, Punleuk Oum, Debashish Saha, Gegi Thomas, Praveen Venkateswaran
| Challenge: | Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts. |
| Approach: | They propose to use large language models to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts. |
| Outcome: | The proposed model can be used to translate NL into Python and convert it into widely adopted business process definition languages. |
RepoShapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion (2026.findings-acl)
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| Challenge: | Large language models have strong reasoning, coding, and generation capabilities, but retrieval-augmented generation remains difficult under fixed context budgets. |
| Approach: | They propose a coalition-aware context filtering framework supervised by Shapley-style marginal contributions that captures sign effects via teacher-forced probing and computes exact Shaply values for small retrieval sets. |
| Outcome: | Experiments show that RepoShapley improves completion quality while reducing harmful context and unnecessary retrieval. |
Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)
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| Challenge: | Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries. |
| Approach: | They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider . |
| Outcome: | The proposed learning scheme improves on a large-scale text-to-SQL data set. |