A Reflection on Reflection 70B: What It Teaches Us About LLM Evaluation and the Role of Private Benchmarking
As we all saw how this roller coaster of excitement and skepticism played out in the community with the announcement of Reflection 70B, it’s a reminder to critically assess the performance claims of new models before fully embracing the hype. And it’s a good reminder to take a step back and fact-check the claims made about new models before jumping on the bandwagon.
A supposedly game-changing, open-source model (and a fine-tuning technique) that would give GPT-4 and Claude a run for their money ended up being a lot of drama.
The Reflection 70B drama is a reminder of the limitations and potential pitfalls of relying solely on public benchmarks. It also underscores the importance of internal model evaluations to ensure real-world performance, relevance, and transparency.
The Problem with Public Benchmarks
One key lesson from the Reflection 70B incident (and others like it, which happen often) is the problem with models being overfitted to public benchmarks. These models might be optimized to excel on specific datasets—like those used in popular benchmarks such as MMLU or GSM8K—but can fail in broader, real-world applications.
This issue is a classic case of Goodhart’s Law: “When a measure becomes a target, it ceases to be a good measure.”
Why Internal Benchmarks Matter
To mitigate the problems with public benchmarks, companies should focus on internal evaluations tailored to their specific needs. Here’s why:
- Relevance: You test what actually matters for your use case. Internal benchmarks ensure that models are tested against tasks and scenarios that matter to your organization. Rather than optimizing for academic-style benchmarks, models are evaluated based on how well they perform in the contexts where they’ll actually be used.
- Avoiding Overfitting: When benchmarks are proprietary and internal, it’s much harder for models to be overfitted to them. This ensures that model performance remains generalizable to real-world tasks.
Implementing Internal Benchmarking
Luckily, there are a variety of tools that can help implement internal model evaluations, from open-source solutions to enterprise-grade platforms like Eval Studio from H2O.ai. Yet many evaluation frameworks emerge regularly and often do similar things. Here are a few to consider:
- RAGAs: Built specifically for RAG pipelines, offering metrics like faithfulness, answer relevancy, and contextual precision.
- Deepchecks: Focuses on evaluating LLMs directly, with dashboard visualizations and monitoring, though the setup can be a bit challenging.
- Phoenix: An observability platform that enables tracing, evaluation, dataset management, and experimentation for LLM applications.
- DeepEval: An LLM evaluation framework that offers unit testing for LLM outputs, supporting popular metrics like G-Eval and RAGAS.
- MLFlow LLM Evaluate: Many of us might have already worked with MLFlow. MLflow has also recently added integration into evaluation pipelines, particularly for RAG and QA evaluations.
While these tools are excellent for various use cases, enterprise environments often require more advanced capabilities. For such cases, solutions like H2O Eval Studio offer a comprehensive, enterprise-grade platform to manage internal benchmarks and run detailed evaluations across multiple models. It provides a rich set of evaluators to cover a wide range of use cases. Eval Studio allows you to create internal leaderboard comparisons and provides deeper insights into model performance, whether for LLMs themselves or applications like RAG, ensuring transparency and performance tracking for your organization’s LLM-based systems.
Explore H2O Eval Studio: https://h2o.ai/platform/enterprise-h2ogpte/eval-studio/.
Conclusion: Moving Towards More Trustworthy AI Evaluations
The Reflection 70B controversy is a reminder that we need better, more transparent ways to evaluate LLMs. Public benchmarks are important, but they’re not the whole story. Internal evaluations, tailored to real-world needs, offer a more reliable way to ensure that models are fit for purpose.
If we aim to build production-ready LLM-based applications, it’s inevitable to adopt an evaluation-first mindset. Every step of the application development process should include strong evaluation measures to ensure that the models and systems we deploy are aligned with real-world requirements, reliable under various conditions, and continuously monitored for performance.
Whether using open-source tools or enterprise solutions like H2O Eval Studio, internal benchmarking is key to ensuring that models are robust, reliable, and ready for real-world challenges.
#LLM #MachineLearning #ModelEvaluation #Reflection
Disclaimer: I am an employee of H2O.ai.
