Papers by Maharshi Gor
MATE: Multi-view Attention for Table Transformer Efficiency (2021.emnlp-main)
Copied to clipboard
| Challenge: | Tables are ubiquitous on the web, and are rich in information. |
| Approach: | They propose a sparse-attention Transformer architecture for modeling documents that contain large tables. |
| Outcome: | The proposed architecture scales linearly with respect to speed and memory, and can handle documents containing more than 8000 tokens with current accelerators. |
Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models have led to claims of AI surpassing humans in QA tasks . authors: models are purportedly acing tests that many humans find challenging . |
| Approach: | They propose a framework that enables quantitative assessment and comparison of problem-solving abilities in QA agents. |
| Outcome: | The proposed framework uncovers distinctficiency patterns in knowledge domains and reasoning skills. |
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness (2025.naacl-long)
Copied to clipboard
| Challenge: | As models evolve, datasets can become outdated. |
| Approach: | They propose a human-grounded evaluation metric that assesses adversarialness by capturing models’ and humans’ varying abilities, while also identifying poor examples. |
| Outcome: | The proposed evaluation metric measures the accuracy of an adversarial question answering dataset and determines whether models are performing well on the dataset. |
AI, Take the Wheel: What Drives Delegation and Trust in Human–Computer Cooperative Question Answering? (2026.findings-acl)
Copied to clipboard
Maharshi Gor, Yoo Yeon Sung, Yu Hou, Eve Fleisig, Zhu Irene Ying, Tianyi Zhou, Jordan Lee Boyd-Graber
| Challenge: | Human-AI collaboration is already happening, both in proactive delegation and deliberative adoption settings. |
| Approach: | They study delegating a task to AI without seeing its output and evaluating AI suggestions to decide whether to adopt them how AI output shapes final decisions. |
| Outcome: | The proposed game pairs 23 experts with 16 AI agents, capturing 387 delegation and 1440 adoption decisions. |
Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing question answering datasets lack diversity in gender, profession, and nationality. |
| Approach: | They focus on how well QA models generalize across demographic subsets . english-language QA datasets mostly ask about US men from a few professions - this is problematic because most English speakers are not from the US or UK . |
| Outcome: | The proposed model accuracy is lower for people based on gender, profession, and nationality, but there is more variation on professions (question topic) and question ambiguity. |