Papers by Kang An
CURE: Context- and Uncertainty-Aware Mental Disorder Detection (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to detect mental disorders focus on the presence of symptoms, but the context of symptoms is often ignored, leading to errors in symptom identification. |
| Approach: | They propose to use large language models to extract contextual information while introducing an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values. |
| Outcome: | The proposed model detects mental disorders even in situations where symptom information is incomplete. |
Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies show that direct generation of diagram descriptions is costly and biased against blind and low-vision (BLV) users. |
| Approach: | They ask sighted individuals to assess diagram descriptions generated by vision-language models . they use latent supervision to guide the models with latent inference . |
| Outcome: | The results show that visual descriptions generated by vision-language models are effective and useful to educators who are themselves BLV and teach visually impaired learners. |
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has demonstrated unprecedented capabilities in sophisticated linguistic comprehension and generative tasks. |
| Approach: | They propose a framework for search LLMs that trains with step-wise proximal policy optimization method to improve QA performance. |
| Outcome: | The proposed framework outperforms global-reward benchmarks on multi-hop QA with a stepwise proximal policy optimization method and richer and more detailed intermediate search rewards and token-level process supervision. |
HMoE: Heterogeneous Mixture of Experts for Language Modeling (2025.emnlp-main)
Copied to clipboard
An Wang, Xingwu Sun, Ruobing Xie, Shuaipeng Li, Jiaqi Zhu, Zhen Yang, Pinxue Zhao, Weidong Han, Zhanhui Kang, Di Wang, Naoaki Okazaki, Cheng-zhong Xu
| Challenge: | Mixture of Experts (MoE) models use homogeneous experts with diverse capacities, resulting in a lack of expert specialization and parameter utilization. |
| Approach: | They propose a framework where experts differ in size and possess diverse capacities . they propose HMoE to encourage frequent activation of smaller experts . |
| Outcome: | The proposed framework outperforms homogeneous homogenous MoE models on evaluation benchmarks and achieves lower loss rate with fewer activated parameters. |
Sparsifying Mamba (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing attempts to integrate sparsification with Mamba fail to leverage Mamba's internal structure for fine-grained sparsifying. |
| Approach: | They propose to use Mamba to integrate sparsification into Mamba and propose a flexible and effective mechanism for parameter scalability. |
| Outcome: | The proposed framework can independently achieve parameter scalability and has stronger performance. |
PLEX: Adaptive Parameter-Efficient Fine-Tuning for Code LLMs using Lottery-Tickets (2025.naacl-industry)
Copied to clipboard
| Challenge: | PLEX is a lottery-ticket based parameter-efficient fine-tuning method that adapts large language models to well-supported and underrepresented programming languages (PLs) in pretraining. |
| Approach: | They propose a lottery-ticket based parameter-efficient fine-tuning method that adapts large language models to well-supported and underrepresented programming languages (PLs) |
| Outcome: | The proposed method achieves state-of-the-art performance among PEFT methods while maintaining competitive results with reduced computational overhead. |