Papers by Shengmin Piao

3 papers
LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction (2026.findings-eacl)

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Challenge: Existing methods rely on proprietary models to generate SQL queries.
Approach: They propose a lightweight framework that translates natural language questions into SQL queries.
Outcome: The proposed framework achieves 72.10% execution accuracy on BIRD and 88.45% on Spider 1.0 . it offers a practical solution for privacy-sensitive and resource-constrained settings.
TinyThinker: Distilling Reasoning through Coarse-to-Fine Knowledge Internalization with Self-Reflection (2025.naacl-long)

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Challenge: Large Language Models exhibit impressive reasoning capabilities across diverse tasks . direct training on synthesized reasoning data may lead to superficial imitation of reasoning process, authors argue .
Approach: They propose a framework that introduces a three-stage process that incrementally guides the student model through the reasoning process, progressively refining knowledge from coarse to fine granularity.
Outcome: The proposed framework achieves superior performance on commonsense reasoning benchmarks and can be extended to other knowledge-intensive reasoning tasks.
SpiralThinker: Latent Reasoning through an Iterative Process with Text–Latent Interleaving (2026.findings-acl)

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Challenge: Existing latent reasoning methods lack mechanisms to ensure stable reasoning dynamics in latent space and a systematic way to interleave implicit and explicit reasoning.
Approach: They propose a framework that performs iterative updates over latent representations while enabling interleaved reasoning across latent and textual steps.
Outcome: SpiralThinker achieves state-of-the-art among latent reasoning baselines.

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