Papers by SooHwan Eom
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback (2024.findings-acl)
Copied to clipboard
Eunseop Yoon, Hee Suk Yoon, SooHwan Eom, Gunsoo Han, Daniel Nam, Daejin Jo, Kyoung-Woon On, Mark Hasegawa-Johnson, Sungwoong Kim, Chang Yoo
| Challenge: | Existing approaches to provide token-level rewards fail to account for varying degrees of preference inherent to each token. |
| Approach: | They propose a reward model that uses a discriminator to assign token-based continuous rewards to each token considering the context. |
| Outcome: | Extensive experiments show that the proposed reward model improves on open-ended language generation benchmarks. |
Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models that capture multiple modalities with a single input length are unable to handle this computational burden. |
| Approach: | They propose a query-based cross-modal projector that compresses visual tokens based on input through the cross-attention mechanism. |
| Outcome: | The proposed projector reduces the need for manually designing the 2D scan order of original image features when converting them into an input sequence for Mamba LLMs. |