Papers by Ashish Kundu
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)
Copied to clipboard
An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
| Challenge: | Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. |
| Approach: | They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks. |
| Outcome: | The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice. |
Role-Conditioned Refusals: Evaluating Access Control Reasoning in Large Language Models (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) blur role boundaries by producing unrestricted responses. |
| Approach: | They propose to extend the Spider and BIRD text-to-SQL datasets with real-time PostgreSQl role-based policies at the table and column levels. |
| Outcome: | The proposed model improves refusal precision and lowers false permits. |
Exploiting Explicit Paths for Multi-hop Reading Comprehension (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to multi-hop reading comprehension do not include multiple sentences or passages. |
| Approach: | They propose a path-based reasoning approach for a multi-hop reading comprehension task . they propose to extract paths from text and compose them to encode them . |
| Outcome: | The proposed model outperforms previous models on the multi-hop Wikihop dataset and can be generalized to the OpenBookQA dataset. |