Papers with non-parametric method
Investigating the Personality Consistency in Quantized Role-Playing Dialogue Agents (2024.emnlp-industry)
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| Challenge: | Using the Big Five personality traits model, we evaluate how stable assigned personalities are for Quantized Role-Playing Dialog Agents (QRPDA) during multi-turn interactions. |
| Approach: | They propose a non-parametric method to evaluate the stability of assigned personalities in quantized large language models (LLMs) for role-playing scenarios. |
| Outcome: | The proposed method shows that it maintains consistent personality traits in QRPDA, and it is more reliable in real-world applications. |
Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)
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| Challenge: | End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters. |
| Approach: | They propose a non-parametric method that leverages in-domain text translation corpus to achieve domain adaptation for E2E-ST systems. |
| Outcome: | The proposed method outperforms the existing in-domain fine-tuning strategies on the Europarl-ST benchmark. |
kNN For Whisper And Its Effect On Bias And Speaker Adaptation (2025.findings-naacl)
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| Challenge: | Speech recognition performance varies by language, domain, speaker characteristics such as accent . fine-tuning models on any of these categories may lead to catastrophic forgetting . |
| Approach: | They propose a nonparametric method that adapts to any category without training the model. |
| Outcome: | The proposed method improves the performance of a transformer end-to-end speech model on transcribed audio in over 680,000 hours of training data on a wide variety of languages. |
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs (2025.emnlp-main)
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| Challenge: | Tabular data is critical across diverse domains, yet high-quality tabular datasets remain scarce due to privacy concerns and the cost of collection. |
| Approach: | They propose a lightweight generative framework that captures sparse dependencies via an LLM-induced graph. |
| Outcome: | The proposed framework reduces constraint violations by 4% and accelerates generation by nearly 9,500 over diffusion-based methods. |
Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)
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Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han, Ling Wang, Xu Shao, Wenqiu Zeng
| Challenge: | Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging. |
| Approach: | They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model. |
| Outcome: | The proposed method is comparable to existing methods and comparable to those using historical data. |