Papers by Disha Disha
Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis (2026.eacl-long)
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| Challenge: | Membership inference attacks (MIAs) reveal whether specific data was used to train machine learning models, serving as important tools for privacy auditing and compliance assessment. |
| Approach: | They propose to examine LLMs’ internal representations rather than just their outputs to gain additional insights into potential membership inference signals. |
| Outcome: | The proposed framework yields strong membership detection across several model families achieving average AUC scores of 0.85 on popular MIA benchmarks. |
Don’t Shoot The Breeze: Topic Continuity Model Using Nonlinear Naive Bayes With Attention (2024.emnlp-industry)
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| Challenge: | Large-scale language models (LLMs) are becoming increasingly popular in business scenarios, but maintaining topic continuity is a challenge. |
| Approach: | They propose a topic continuity model that assesses whether a response aligns with the initial conversation topic using a Naive Bayes approach. |
| Outcome: | The proposed model outperforms existing models in handling lengthy and complex conversations. |
BIG-Bench Extra Hard (2025.acl-long)
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Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat
| Challenge: | Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. |
| Approach: | They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning . |
| Outcome: | The new benchmark pushes the boundaries of LLM reasoning evaluation. |
Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection (2020.coling-main)
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| Challenge: | Existing work on identifying the salient information in a text has used a limited representation of events that omits essential information. |
| Approach: | They propose a highly contextual model of event salience that uses a rich representation of events and integrates document-level information. |
| Outcome: | The proposed model improves on an event salience dataset by 2-4% on standard metrics and addresses flaws in existing evaluation methodologies. |
Annotation of a Large Clinical Entity Corpus (D18-1)
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| Challenge: | Past researches have shown the superiority of statistical/ML approaches over the rule based approaches. |
| Approach: | They propose to annotate a clinical domain annotated corpus using a small data set or a narrower domain to take full advantage of machine learning. |
| Outcome: | The proposed corpus contains 5,160 clinical documents from forty different clinical specialties. |
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages (2021.findings-acl)
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| Challenge: | Model-agnostic meta-learning (MAML) is a strategy to learn resource-poor languages in a sample-efficient fashion. |
| Approach: | They propose a model-agnostic meta-learning strategy that minimizes the expected risk across languages with a uniform prior . they propose 'minimax' and 'neyman-pearson' models that constrain the risk in each language to a maximum threshold. |
| Outcome: | The proposed model reduces the maximum risk across languages while constraining the risk in each language to a maximum threshold. |