Papers by Karttikeya Mangalam
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (2024.findings-acl)
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Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael Mahoney, Kurt Keutzer, Amir Gholami
| Challenge: | Pretrained large language models are currently state-of-the-art for solving most tasks . however, many of them are in the low-data regime, making fine-tuning challenging . a new data augmentation strategy uses a teacher LLM to augment a small seed dataset . |
| Approach: | They propose a targeted and iterative data augmentation strategy that augments a teacher LLM to fine-tune a small seed dataset by adding additional data. |
| Outcome: | The proposed approach outperforms fine-tuning and other data augmentation strategies on a small seed dataset. |
Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction (2024.findings-naacl)
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Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Weinberger, Jitendra Malik, Trevor Darrell, Dan Klein
| Challenge: | Recent studies show multimodal inputs can improve grammar induction, but weak textual baselines are needed for training. |
| Approach: | They use a fixed grammar family to compare multimodal grammar induction methods . they find multimodal inputs can improve grammar induction by grounding textual inputs to the visual world . |
| Outcome: | The proposed model outperforms weaker baselines on four benchmark datasets. |