Papers by Aditya Rawal
Split-Merge: Scalable and Memory-Efficient Merging of Expert LLMs (2025.emnlp-main)
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| Challenge: | a zero-shot merging framework for large language models consolidates specialized domain experts into a single model without any further training. |
| Approach: | They propose a zero-shot merging framework that consolidates specialized domain experts into a single model without further training. |
| Outcome: | Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of the proposed framework. |
Extreme Miscalibration and the Illusion of Adversarial Robustness (2024.acl-long)
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| Challenge: | emergence of the Adversarial Training paradigm has shown some success in training models to be more robust to these small adversarial perturbations. |
| Approach: | They propose to use adversarial examples to detect adversarials by miscalibrating models to mask gradients in a way that interferes with adversarial attack search methods. |
| Outcome: | The proposed model gains are an illusion of robustness (IOR) and urge the NLP community to incorporate test-time temperature scaling into their robustness evaluations to ensure that any observed gains are genuine. |
DEM: Distribution Edited Model for Training with Mixed Data Distributions (2024.emnlp-main)
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| Challenge: | Recent fine-tuning approaches for large language models require supervised finetun on diverse datasets and follow different distributions. |
| Approach: | They propose a distribution edited model that integrates models individually trained on each data source with the base model using basic element-wise vector operations. |
| Outcome: | The proposed model outperforms baseline models on a variety of benchmarks and is cheaper than standard data mixing methods. |
Sequence-level Large Language Model Training with Contrastive Preference Optimization (2025.findings-naacl)
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| Challenge: | a new method to improve the performance of large language models requires a small computational cost. |
| Approach: | They propose a CPO procedure that can inject sequence-level information into the model at any training stage without expensive human labeled data. |
| Outcome: | The proposed objective surpasses the next token prediction in terms of win rate in instruction-following and text generation tasks. |