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cs.CL2026

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…

cs.CL2026

SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks

Mohammadtaher Safarzadeh, Hitesh Laxmichand Patel, Afshin Orojlooyjadid +2

Large language models (LLMs) have achieved strong performance on natural language to SQL (NL2SQL) benchmarks, yet their reported accuracy may be inflated by contamination from benc…

cs.CL2026

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…

cs.CL2025

OraPlan-SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoning

Marianne Menglin Liu, Sai Ashish Somayajula, Syed Fahad Allam Shah +2

We present OraPlan-SQL, our system for the Archer NL2SQL Evaluation Challenge 2025, a bilingual benchmark requiring complex reasoning such as arithmetic, commonsense, and hypotheti…

cs.CL2025

Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs

Jyotika Singh, Weiyi Sun, Amit Agarwal +4

In modern industry systems like multi-turn chat agents, Text-to-SQL technology bridges natural language (NL) questions and database (DB) querying. The conversion of tabular DB resu…

cs.CL2025

Aligning LLMs for Multilingual Consistency in Enterprise Applications

Amit Agarwal, Hansa Meghwani, Hitesh Laxmichand Patel +3

Large language models (LLMs) remain unreliable for global enterprise applications due to substantial performance gaps between high-resource and mid/low-resource languages, driven b…