Papers by Xintao Wu
Let The Jury Decide: Fair Demonstration Selection for In-Context Learning through Incremental Greedy Evaluation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing demonstration selection strategies focus on optimizing performance metrics such as accuracy. |
| Approach: | They propose a framework for selecting fair and representative demonstrations that improve group fairness in In-Context Learning. |
| Outcome: | The proposed framework improves fairness metrics without compromising accuracy. |
Soft Prompting for Unlearning in Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing ethical and safety considerations for large language models are important for deployment . however, some ethical concerns have been raised due to the presence of private, sensitive, or harmful information in the training data. |
| Approach: | They propose a framework that learns prompt tokens that are prepended to a query to induce unlearning in LLMs. |
| Outcome: | The proposed method improves the trade-off between utility and forgetting for text classification and question-answering. |
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large vision-language models have shown impressive ability in various language tasks, especially with their emergent in-context learning capability. |
| Approach: | They propose a causal reasoning benchmark for multi-modal in-context learning from large vision-language models that incorporates visual inputs. |
| Outcome: | The proposed model outperforms existing models on three visual causal reasoning tasks and demonstrates their strengths and weaknesses. |
Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)
Copied to clipboard
Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu
| Challenge: | Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules . |
| Approach: | They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain . |
| Outcome: | The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information . |