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Ishani Mondal

9 accepted papers

2025

ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly

Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek +2
COLING 2025main

Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s n…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)

SUBBA REDDY OOTA, Akshett Rai Jindal, Ishani Mondal, Khushbu Pahwa, Satya Sai Srinath Namburi GNVV, Manish Shrivastava +3
ICLR 2025poster

Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models—through increased size, instruction-tuning, and multimodality—has led to better representational alignment with neural data…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness

Yoo Yeon Sung, Maharshi Gor, Eve Fleisig, Ishani Mondal, Jordan Lee Boyd-Graber
NAACL 2025long

Adversarial datasets should validate AI robustness by providing samples on which humans perform well, but models do not. However, as models evolve, datasets can become obsolete. Measuring whether a dataset remains adversarial is hindered by the lack of a standardized metric for measuring adversarial…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

PEDANTS: Cheap but Effective and Interpretable Answer Equivalence

Zongxia Li, Ishani Mondal, Huy Nghiem, Yijun Liang, Jordan Lee Boyd-Graber
EMNLP 2024finding

Question answering (QA) can only make progress if we know if an answer is correct, but current answer correctness (AC) metrics struggle with verbose, free-form answers from large language models (LLMs). There are two challenges with current short-form QA evaluations: a lack of diverse styles of eval…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2024

SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement

Ishani Mondal, Zongxia Li, Yufang Hou, Anandhavelu Natarajan, Aparna Garimella, Jordan Lee Boyd-Graber
EMNLP 2024finding

Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and accelerating the process. Current text-to-image models (Rombach et al., 2022a; Belouadi et al., 2023) struggle with gener…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2023

Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues

Shivani Kumar, Ishani Mondal, Md Shad Akhtar, Tanmoy Chakraborty
AAAI 2023technical

Conversations emerge as the primary media for exchanging ideas and conceptions. From the listener’s perspective, identifying various affective qualities, such as sarcasm, humour, and emotions, is paramount for comprehending the true connotation of the emitted utterance. However, one of the major hur…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗
2022

Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic?

Ishani Mondal, Kabir Ahuja, Mohit Jain, Jacki O’Neill, Kalika Bali, Monojit Choudhury
COLING 2022main

The COVID-19 pandemic has brought out both the best and worst of language technology (LT). On one hand, conversational agents for information dissemination and basic diagnosis have seen widespread use, and arguably, had an important role in fighting against the pandemic. On the other hand, it has al…

Cited by 7Source ↗PDF ↗Scholar ↗
2022

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik +29
EMNLP 2022main

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, in…

Cited by 582Source ↗PDF ↗code ↗Scholar ↗