collaborators
Showing cs.CLShow all

10 papers · 1 filter

cs.CL2026

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…

cs.CL2026

MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation

Jyotika Singh, Fang Tu, Miguel Ballesteros +6

Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational turns, yet multi-turn (MT) i…

cs.CL2026

GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations

Jyotika Singh, Fang Tu, Aziza Mirsaidova +11

Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed test sets. Most robustness va…

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

DiffuMask: Diffusion Language Model for Token-level Prompt Pruning

Caleb Zheng, Jyotika Singh, Fang Tu +6

In-Context Learning and Chain-of-Thought prompting improve reasoning in large language models (LLMs). These typically come at the cost of longer, more expensive prompts that may co…

cs.CL2026

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…