Papers by Chun-Fu Yeh
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model (2024.emnlp-industry)
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Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar
| Challenge: | Prior work on LLMs focused on models that combine text and one other modality, such as image encoders or proprietary models that are not open sourced. |
| Approach: | They propose a unified model that reasons over diverse input modality signals and generates textual responses. |
| Outcome: | The proposed model performs better on multimodal tasks than industry-leading models . |