Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration
arXiv:2603.02760v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) have recently attracted significant attention for their ability to enhance diversity, controllability, and parallelism. However, their non-sequential, bidirectionally masked generation makes quality assessment difficult, underscoring the need for effective self-evaluation. In this work, we propose DiSE, a simple yet effective self-evaluation confidence quantification method for dLLMs. DiSE quantifies confidence by computing the probability of regenerating the tokens in the entire generated sequence, given the full context. This method enables more efficient and reliable quality assessment by leveraging token regeneration probabilities, facilitating both likelihood estimation and robust uncertainty quantification. Building upon DiSE, we further introduce a flexible-length generation framework, which adaptively controls the sequence length based on the model's self-assessment of its own output. We an
arXiv:2603.02760v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) have recently attracted significant attention for their ability to enhance diversity, controllability, and parallelism. However, their non-sequential, bidirectionally masked generation makes quality assessment difficult, underscoring the need for effective self-evaluation. In this work, we propose DiSE, a simple yet effective self-evaluation confidence quantification method for dLLMs. DiSE quantifies confidence by computing the probability of regenerating the tokens in the entire generated sequence, given the full context. This method enables more efficient and reliable quality assessment by leveraging token regeneration probabilities, facilitating both likelihood estimation and robust uncertainty quantification. Building upon DiSE, we further introduce a flexible-length generation framework, which adaptively controls the sequence length based on the model's self-assessment of its own output. We analyze and validate the feasibility of DiSE from the perspective of dLLM generalization, and empirically demonstrate that DiSE is positively correlated with both semantic coherence and answer accuracy. Extensive experiments on likelihood evaluation, uncertainty quantification, and flexible-length generation further confirm the effectiveness of the proposed DiSE.
Executive Summary
This article proposes DiSE, a novel self-evaluation method for diffusion large language models (dLLMs) that enables efficient and reliable quality assessment. DiSE quantifies confidence by computing the probability of regenerating the tokens in the entire generated sequence, given the full context. The authors also introduce a flexible-length generation framework that adaptively controls sequence length based on the model's self-assessment. The proposed method is empirically demonstrated to be positively correlated with semantic coherence and answer accuracy. The article presents extensive experiments that validate the effectiveness of DiSE in likelihood evaluation, uncertainty quantification, and flexible-length generation. This work has significant implications for the development and application of dLLMs in various fields.
Key Points
- ▸ DiSE is a novel self-evaluation method for dLLMs that enables efficient and reliable quality assessment.
- ▸ DiSE quantifies confidence by computing the probability of regenerating the tokens in the entire generated sequence.
- ▸ The proposed method is empirically demonstrated to be positively correlated with semantic coherence and answer accuracy.
Merits
Strength
DiSE provides a novel and effective solution to the quality assessment problem in dLLMs, which is a significant contribution to the field.
Demerits
Limitation
The proposed method relies on the assumption that the generated sequence is an accurate representation of the underlying data distribution, which may not always be the case in real-world applications.
Expert Commentary
The proposed method is a significant contribution to the field of deep learning, particularly in the area of model evaluation and validation. The use of sequence regeneration probabilities to quantify confidence is a novel and effective approach that has been empirically demonstrated to be positively correlated with semantic coherence and answer accuracy. However, the proposed method relies on the assumption that the generated sequence is an accurate representation of the underlying data distribution, which may not always be the case in real-world applications. Therefore, further research is needed to investigate the robustness and reliability of the proposed method in various scenarios. Additionally, the article highlights the importance of providing robust and reliable quality assessment methods for dLLMs, which has significant policy implications for the development and deployment of AI systems in regulatory and governance frameworks.
Recommendations
- ✓ Further research is needed to investigate the robustness and reliability of the proposed method in various scenarios.
- ✓ The proposed method should be integrated into existing frameworks for model evaluation and validation to ensure its practical applicability.
