1 citations · 1 across the 14 of their papers we have counts for
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PLSQLBench: Benchmarking LLM Systems for Executable Procedural Database Programming
Marianne Menglin Liu, Leonid Boytsov, Daniel W. Peterson +13
We present PLSQLBench, to our knowledge the first benchmark for evaluating whether LLMs can write executable PL/SQL programs, with correctness measured through execution-based test…
SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing
Sai Ashish Somayajula, Marianne Menglin Liu, Chuan Lei +9
Natural language interfaces to databases aim to translate user questions into executable SQL, yet remain brittle in real-world settings where questions are underspecified and schem…
Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss
Meizhu Liu, Matthew Rowe, Amit Agarwal +8
Audio-text retrieval enables semantic alignment between audio content and natural language queries, supporting applications in multimedia search, accessibility, and surveillance. H…
Au-M-ol: A Unified Model for Medical Audio and Language Understanding
Meizhu Liu, Nistha Mitra, Paul Li +3
In this work, we present Au-M-ol, a novel multimodal architecture that extends Large Language Models (LLMs) with audio processing. It is designed to improve performance on clinical…
LLM NL2SQL Robustness: Surface Noise vs. Linguistic Variation in Traditional and Agentic Settings
Lifu Tu, Rongguang Wang, Tao Sheng +2
Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conv…
Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning
Jiashu He, Meizhu Liu, Olaitan P Olaleye +9
Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer f…