Papers by Yash Mathur
De-Identification of Sensitive Personal Data in Datasets Derived from IIT-CDIP (2024.emnlp-main)
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Stefan Larson, Nicole Lima, Santiago Diaz, Amogh Joshi, Siddharth Betala, Jamiu Suleiman, Yash Mathur, Kaushal Prajapati, Ramla Alakraa, Junjie Shen, Temi Okotore, Kevin Leach
| Challenge: | Large volumes of data are becoming increasingly important for training machine learning models for document understanding tasks like classification, information extraction, and visual question answering. |
| Approach: | They propose a data de-identification pipeline that replaces sensitive data with synthetic, but realistic, data that preserves the utility of de-identified documents. |
| Outcome: | The proposed method preserves the utility of the de-identified documents so that they can continue to be used in various document understanding applications. |
Program-Aided Reasoners (Better) Know What They Know (2024.naacl-long)
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| Challenge: | Prior work shows that program-aided reasoning improves accuracy but also requires reasoners to "know what they know". |
| Approach: | They compare the calibration of program-aided language models (PAL) and text-based Chain-of-thought (COT) prompting techniques over 5 datasets and 2 model types . |
| Outcome: | The proposed methods improve accuracy and calibrate the models over 5 datasets and 2 model types. |
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics (2026.findings-acl)
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Atharva Naik, null Prakam, Yash Mathur, Darsh Agrawal, Manav Nitin Kapadnis, Yuwei An, Clayton Marr, Carolyn Rose, David R. Mortensen
| Challenge: | a benchmark for inductive reasoning is based on sound law induction in historical linguistics . solve rates are below 5% on hard PBEBench instances with long program cascades despite expensive scaling strategies . |
| Approach: | They propose a benchmark for inductive reasoning inspired by sound law induction in historical linguistics. |
| Outcome: | The proposed approach generates problems with controllable difficulty and ordering constraints . solve rates remain below 5% on hard PBEBench instances with long program cascades . |