Papers by Sanjiv Kumar
Regression Aware Inference with LLMs (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. |
| Approach: | They propose alternative inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. |
| Outcome: | The proposed approach significantly improves over baselines across datasets and models. |
Large Language Models with Controllable Working Memory (2023.findings-acl)
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Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
| Approach: | They propose a method to inject counterfactual and irrelevant contexts into standard supervised datasets to strengthen both controllability and robustness. |
| Outcome: | The proposed method improves controllability and robustness across model architectures and sizes. |
Semantic Label Smoothing for Sequence to Sequence Problems (2020.emnlp-main)
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Michal Lukasik, Himanshu Jain, Aditya Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix Yu, Sanjiv Kumar
| Challenge: | Existing methods for seq2seq regularization use label smoothing, but it is difficult to extend it to other datasets. |
| Approach: | They propose a method that smooths over well formed relevant sequences that are semantically similar to the target sequence. |
| Outcome: | The proposed method shows a consistent and significant improvement over the state-of-the-art methods on different datasets. |