Beyond Benchmark Leaders: The risks of model selection in the age of LLMs
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The race to adopting the best-performing models on public benchmarks might not always be the wisest decision when building customer facing applications. This decision is even more critical when dealing with large language models given the scale of data used in their training and the availability of open-weighted models.
The Limitations of Generic Benchmarks
While standardized benchmarks like MMLU, HELM, and GLUE provide valuable comparative metrics, they may not fully reflect a model’s performance in specific real-world applications. These benchmarks typically evaluate models across general tasks, but your specific use case might require specialized capabilities or domain knowledge that aren’t captured in these standardized tests.
The Model Collapse Phenomenon
A critical consideration when selecting models, especially those built on top of existing architectures, is the risk of model collapse. This degenerative process occurs when models are repeatedly trained on data generated by other models, leading to:
- Progressive loss of information about the true underlying data distribution
- Disappearance of distribution tails
- Convergence to limited point estimates with minimal variance
This phenomenon becomes particularly concerning in the context of LLMs, which are often initialized with pre-trained models and then fine-tuned for specific tasks.
The Case for Custom Benchmarking
Why Custom Benchmarks Matter Custom benchmarking allows you to:
- Evaluate performance specific to your use case
- Assess model behavior in your domain context
- Measure metrics that matter for your application
Key Considerations for Evaluation
When developing custom benchmarks, focus on:
- Task-specific performance metrics
- Domain-specific requirements
- Real-world application scenarios
The Value of Model Stability
While peak performance is important, model stability and longevity should be key considerations. A model that performs consistently well over time may be preferable to one that shows slightly better benchmark scores but risks degradation through repeated fine-tuning.
Practical Recommendations
Documentation and Monitoring Keep detailed records of:
- Model parameters and configurations
- Training data sources
- Performance metrics specific to your use case
Data Management When fine-tuning models:
- Preserve original training data
- Accumulate new data rather than replacing existing datasets
- Maintain a balance between synthetic and real data
Conclusion
The path to selecting the right model extends beyond leaderboard positions. Consider the long-term stability, specific use case requirements, and potential risks of model collapse when making your selection. Remember that the best-performing model on public benchmarks might not be the optimal choice for your specific needs.