31 citations · 63 across the 14 of their papers we have counts for
13 papers · 1 filter
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
Linyong Nan, Ellen Zhang, Weijin Zou +3
This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models (LLMs) interact with a SQL interpreter. The task necessitat…
DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
Yilun Zhao, Yitao Long, Hongjun Liu +7
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-wo…
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Yilun Zhao, Chen Zhao, Linyong Nan +5
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task…
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies
Linyong Nan, Yilun Zhao, Weijin Zou +5
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context th…
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
Yilun Zhao, Haowei Zhang, Shengyun Si +3
Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The ad…
QTSumm: Query-Focused Summarization over Tabular Data
Yilun Zhao, Zhenting Qi, Linyong Nan +9
People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' informat…