Papers by Dehong Gao
ASPO: Adaptive Sentence-Level Preference Optimization for Fine-Grained Multimodal Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances have extended DPO to multimodal scenarios, achieving strong performance. |
| Approach: | They propose to use a sentence-level preference optimization technique to optimize individual sentences for more precise preference optimization without additional models or parameters. |
| Outcome: | Experiments show that Adaptive Sentence-level Preference Optimization significantly improves the alignment of multimodal models. |
Self-Renewal Prompt Optimizing with Implicit Reasoning (2024.findings-emnlp)
Copied to clipboard
Zihan Liang, Ben Chen, Zhuoran Ran, Zihan Wang, Huangyu Dai, Yufei Ma, Dehong Gao, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in NLP have been driven by the development of Large Language Models (LLMs). |
| Approach: | They propose a self-renewal approach to optimize LLM outputs to better align with human preferences without supervised fine-tuning. |
| Outcome: | The proposed approach improves outputs to better align with human preferences across LLMs and tasks without supervised fine-tuning. |
Dependency Position Encoding for Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to extract relation extraction from sentence are limited in focusing on leveraging dependency information. |
| Approach: | They propose dependency position encoding (DPE) that incorporates dependency connections and dependency types into the self-attention mechanism to distinguish the importance of different word dependencies. |
| Outcome: | The proposed method significantly outperforms the previous methods on SemEval 2010 Task 8, KBP37, and TACRED. |
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning (2024.emnlp-main)
Copied to clipboard
Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen, Dehong Gao, Zhuoran Ran, Wang Zihan, Linbo Jin, Wen Jiang, Guannan Zhang, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in open-source Large Language Models (LLMs) have achieved notable successes in natural language processing. |
| Approach: | They propose a Parameter Efficient Fine-Tuning paradigm for improved fine-tuning and parameter efficiency in multi-task learning. |
| Outcome: | The proposed model outperforms existing methods on multi-task learning while reducing training costs by over 80% without losing general capability. |