Papers by Shrey Pandit
A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models (2023.acl-long)
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| Challenge: | Existing literature demonstrates that compressing deep learning models could affect their fairness. |
| Approach: | They evaluate pruned, distilled, and quantized language models to assess their fairness . they also examine the impact of using multilingual models and evaluation measures . |
| Outcome: | The proposed methods can reduce the fairness of language models by reducing their complexity and reducing the cost of training and deployment. |
CIAug: Equipping Interpolative Augmentation with Curriculum Learning (2022.naacl-main)
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| Challenge: | Current methods for interpolative data augmentation select samples at random, which might make it difficult for the model to generalize better and converge faster. |
| Approach: | They propose a curriculum-based learning method that leverages the relative position of samples in hyperbolic embedding space as a complexity measure to gradually mix up increasingly difficult and diverse samples along training. |
| Outcome: | The proposed method achieves state-of-the-art results over existing methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks. |
AdaPT: A Set of Guidelines for Hyperbolic Multimodal Multilingual NLP (2024.findings-naacl)
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| Challenge: | Euclidean space is used for training neural models and performing arithmetic operations, but many data types have complex geometries and cannot be captured in the Euclidesan space. |
| Approach: | They propose a set of guidelines for initialization, parametrization, and training of neural networks that can be generalized over existing neural network training methodologies. |
| Outcome: | The proposed framework outperforms Euclidean methods on three tasks over 12 languages and modalities on a variety of domains. |
MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) generate plausible but factually incorrect outputs, posing serious risks to patient safety and clinical decision-making. |
| Approach: | They propose a benchmark for medical hallucination detection using 10,000 question-answer pairs derived from PubMedQA. |
| Outcome: | The proposed model achieves an F1 score as low as 0.625 for detecting 'hard' category hallucinations. |
Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math (2026.acl-long)
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| Challenge: | Large language model (LLM)-based reasoning systems have recently achieved gold medal-level performance in the IMO 2025 competition . |
| Approach: | They propose a human-annotated step-level verification benchmark that measures step- level verifiers at the frontier. |
| Outcome: | The proposed benchmark outperforms closed-source models in step-level verification and the impact of scaling verifier compute. |
DMix: Adaptive Distance-aware Interpolative Mixup (2022.acl-short)
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| Challenge: | Interpolation-based regularisation methods such as Mixup have shown to be effective for various tasks and modalities. |
| Approach: | They propose an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space. |
| Outcome: | The proposed method achieves state-of-the-art on sentence classification over existing methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations. |