Papers by Duc Anh Do
Discrete Diffusion Language Model for Efficient Text Summarization (2025.findings-naacl)
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| Challenge: | Existing discrete diffusion models fail on conditional long-text generation due to incompatibility between the backbone architectures and the random noising process. |
| Approach: | They propose a semantic-aware noising process that enables Transformer backbones to handle long sequences effectively. |
| Outcome: | The proposed model outperforms existing models on three benchmark summarization datasets while achieving much faster inference speed compared to autoregressive models. |
Evaluating AI for Finance: Is AI Credible at Assessing Investment Risk Appetite? (2025.emnlp-industry)
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Divij Chawla, Ashita Bhutada, Duc Anh Do, Abhinav Raghunathan, Vinod Sp, Cathy Guo, Dar Win Liew, Prannaya Gupta, Rishabh Bhardwaj, Rajat Bhardwaj, Soujanya Poria
| Challenge: | Our analysis was conducted on proprietary systems and open-weight models . FINRISKEVAL analyzed 1,720 profiles spanning a broad spectrum of possible risk categories . |
| Approach: | They evaluated proprietary AI systems and open-weight models to assess investment risk appetite using carefully curated user profiles. |
| Outcome: | The proposed models exhibit significant variance when user attributes that should not influence risk computation are changed. |
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic (2024.acl-long)
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| Challenge: | RESTA is a tool to perform LLM realignment towards safety, which gets compromised due to downstream task fine-tuning. |
| Approach: | They propose to add a safety vector to the weights of a compromised model by arithmetic and demonstrate its generalizability on three existing safety evaluation benchmarks and a multilingual benchmark dataset. |
| Outcome: | The proposed model reduces harmfulness of the model from 18.6% to 5.1% and from 9.2% to 1.5% in parameter-efficient and full fine-tuning while maintaining most of the system's performance on the task. |
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)
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| Challenge: | Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors . |
| Approach: | They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation . |