activity
20182025
most citedThe GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

52 citations · 62 across the 9 of their papers we have counts for

collaborators

13 papers

cs.DB2025

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases

Mounica Maddela, Lingjue Xie, Daniel Preotiuc-Pietro +1

Semantic parsing methods for converting text to SQL queries enable question answering over structured data and can greatly benefit analysts who routinely perform complex analytics…

cs.CL2023

BLESS: Benchmarking Large Language Models on Sentence Simplification

Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4

We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…

cs.CL2023

Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts

Mounica Maddela, Megan Ung, Jing Xu +3

Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack tr…

cs.CL2023★ 1 cited

Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA

David Heineman, Yao Dou, Mounica Maddela +1

Large language models (e.g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding o…

cs.CL2022★ 2 cited

LENS: A Learnable Evaluation Metric for Text Simplification

Mounica Maddela, Yao Dou, David Heineman +1

Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation. However, existing human eval…

cs.CL2022

EntSUM: A Data Set for Entity-Centric Summarization

Mounica Maddela, Mayank Kulkarni, Daniel Preotiuc-Pietro

Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to…