Papers by Chaosheng Dong
AutoEval-ToD: Automated Evaluation of Task-oriented Dialog Systems (2025.naacl-long)
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| Challenge: | Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale. |
| Approach: | They propose an automated end-to-end evaluation framework that interacts with the ToD system and then assesses its performance across key dimensions. |
| Outcome: | The proposed framework first interacts with the ToD system and assesses its performance across key dimensions by analyzing both its responses and internal states. |
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning (2024.findings-naacl)
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| Challenge: | Existing methods for continual prompt tuning are limited by the ever-growing parameter scale of modern language models (e.g., GPT-4 that may have 1.76 trillion parameters). |
| Approach: | They propose a method for continual prompt tuning that enables the lifelong learning of a pre-trained language model by adding a task-specific prompt to a queue of older tasks. |
| Outcome: | The proposed method outperforms the state-of-the-art methods substantially on continual prompt tuning benchmarks. |