Papers by Xiaobao Guo
Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
Copied to clipboard
Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
| Approach: | They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs. |
| Outcome: | The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content. |
P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Defending Large Language Models (LLMs) against backdoors has long been trapped in a "cat-and-mouse" dilemma where defenders passively react to ever-shifting attack strategies. |
| Approach: | They propose a general and effective defense algorithm that implants benign triggers to reshape the model’s decision boundary. |
| Outcome: | The proposed defense algorithm can neutralize malicious backdoors while preserving task performance. |
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to modulate one modal feature to another are lacking in multimodal representation learning. |
| Approach: | They propose to use unimodal and crossmodal refinement networks to enhance uni and cross-modal representations by iterative updating of distributions with transformer-based attention layers to refine modality-specific learning. |
| Outcome: | The proposed network outperforms state-of-the-art techniques on MOSI and MOSEI datasets. |