Multi-token Prediction Models for Large Language Models: Code and Discussion
#2
by
ashishpatel26
- opened
Discussion Points:
Efficacy and Generalizability:
- How do the multi-token models perform compared to baselines on various code-related tasks? Does the performance gap widen with increased data size (1T vs 200B tokens)?
- Does the benefit of multi-token prediction extend beyond code to natural language tasks? Let's explore its generalizability across domains.
Efficiency Considerations:
- While the paper suggests faster inference with multi-token models, the code primarily focuses on running inference. Let's discuss the potential trade-offs between training efficiency (time and resources) and inference speedup. Is the potential speed gain worth the additional training cost, if any?
Optimal Prediction Horizon (n):
- The current implementation uses n=4. Is this the optimal value for all scenarios? How does varying n affect performance and efficiency for different tasks or model sizes? Let's explore the impact of this parameter.
Evaluation Metrics:
- The code doesn't specify evaluation metrics. What metrics are most suitable to compare multi-token and baseline models, particularly for code-related tasks?
Good questions, but who answers?