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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

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

Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth

Michelle Yuan, Weiyi Sun, Amir H. Rezaeian +5

Transformers have become the foundational architecture for a broad spectrum of sequence modeling applications, underpinning state-of-the-art systems in natural language processing,…

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…