Crafting Your Own LLM Yardstick: A Guide to Building Powerful Internal Benchmarks
Why Public Benchmarks Fall Short
The landscape of Large Language Models (LLMs) is evolving at an unprecedented pace, with new models emerging almost weekly. Staying abreast of these developments and discerning which models are truly superior can feel like an insurmountable task. Typically, we rely on public benchmarks and online opinions to guide our choices. However, this approach is fundamentally flawed. Public benchmarks, such as those used to announce breakthroughs, create a powerful incentive for model developers to fine-tune their LLMs specifically to excel on these tests. This optimization means that benchmark performance may not accurately reflect a model's general capabilities or its suitability for your unique requirements.
Furthermore, online opinions, while valuable, are often subjective and based on the reviewer's specific use cases, which may differ significantly from your own. To truly understand how an LLM will perform for your organization's needs, a more tailored approach is necessary: developing your own internal benchmarks.
The Case for Internal LLM Benchmarks
Internal benchmarks offer a crucial advantage: they allow you to evaluate LLMs on tasks and data that are specific to your operational context. By creating benchmarks that are not widely available online or that focus on niche applications, you mitigate the risk of "benchmark contamination," where models may have already been trained on the test data. This ensures a more accurate and relevant assessment of a model's true performance for your use cases.
Developing Your Internal Benchmark: Key Considerations
The creation of an effective internal benchmark hinges on a few core principles. Firstly, the benchmark task should ideally be one that LLMs are not commonly trained on, or it should leverage proprietary data that is not publicly accessible. This ensures that the models are evaluated on their general capabilities rather than on pre-existing knowledge of the benchmark itself. Secondly, the benchmark process must be as automated as possible. Manual testing for every new model release is time-prohibitive and unsustainable. Automation allows for frequent and consistent evaluation, making the benchmarking process a manageable part of your workflow.
Finally, your benchmark should yield a quantifiable, numeric score. This objective measurement is essential for ranking different LLMs against each other and making data-driven decisions about which models to adopt.
Types of Tasks for Internal Benchmarks
Internal benchmarks can take various forms, tailored to specific organizational needs. Here are a few examples:
- For rare programming language development: If your organization uses less common programming languages, you can create benchmarks that test an LLM's ability to generate, debug, or explain code in these specific languages. This moves beyond general coding benchmarks and focuses on your niche requirements.
- Internal question-answering chatbot: Gather a collection of prompts that are representative of actual user queries within your organization. Alongside these prompts, define the desired or correct responses. The benchmark then measures how closely an LLM's generated answers match these ground truths. This is particularly effective when using internal documentation or knowledge bases as the source for expected answers.
- Classification tasks: For use cases like sentiment analysis or topic categorization of internal documents, you can create a dataset of input-output examples. The input would be a piece of text, and the output would be a specific label (e.g., "positive sentiment," "technical support," "sales inquiry"). The evaluation here is straightforward: the LLM's predicted label must exactly match the ground truth label.
Ensuring Automation in Your Benchmark
Once you have identified the task for your internal benchmark, the next critical step is to ensure its automation. The goal is to minimize manual intervention for each new model you test. A recommended approach is to develop a standardized interface for your benchmark. This interface should accept a prompt and return the raw text response from the LLM. When a new model is released, the only modification required is to integrate the new model's API or output mechanism into this interface. The rest of your benchmarking application, including the evaluation logic, can remain static. This significantly reduces the overhead associated with testing new LLMs.
To further streamline the evaluation, consider implementing automated assessment procedures. This could involve running predefined scripts to check for correctness or even employing another LLM as a judge to evaluate the quality of responses, provided the judging LLM is not susceptible to the same biases you are trying to avoid.
Testing LLMs on Your Internal Benchmark
With your internal benchmark established and automated, it's time to put various LLMs to the test. It is advisable to test all prominent closed-source frontier models, such as those from major providers. Equally important is testing open-source releases, as they often offer flexibility and cost advantages.
Whenever a significant new model is announced or released, such as a major update from a leading developer, run it through your benchmark. The low cost of testing, thanks to your automated setup, makes this a feasible practice. Furthermore, it is crucial to run your benchmarks regularly, even for models you have already tested. Models, especially those offered as a service without fixed versioning, can change over time, leading to shifts in their output. Consistent re-evaluation is vital, particularly if a model is deployed in a production environment where maintaining output quality is paramount.
Avoiding Benchmark Contamination
A critical aspect of developing and using internal benchmarks is preventing data contamination. This occurs when the data used in your benchmark is inadvertently exposed online, making it accessible during the LLM's pre-training phase. Since many state-of-the-art LLMs are trained on vast datasets scraped from the internet, if your benchmark data or its solutions are publicly available, the models may have already "learned" the answers. This renders your benchmark ineffective. Therefore, ensure that all data used in your internal benchmarks is kept private and secure.
Optimizing Time Investment
While staying updated on LLM releases is essential, the process of benchmarking should not consume an excessive amount of your time. The value derived from internal benchmarks comes from their ability to provide rapid, relevant insights. Aim to minimize the time spent on this process. When a new frontier model emerges, run it against your benchmark and analyze the results. If a new model demonstrates a substantial improvement over your current best, it warrants further investigation and potential adoption. However, if the gains are only incremental, it may be prudent to wait for subsequent releases. The decision to switch models should also consider practical factors such as the time and cost involved in migration, and any differences in latency between the old and new models.
Conclusion
Developing and maintaining internal LLM benchmarks is a strategic imperative for any organization leveraging AI. It provides a reliable, tailored method for evaluating LLMs, moving beyond the limitations of public benchmarks and subjective opinions. By focusing on unique use cases, ensuring automation, and diligently guarding against contamination, you can create a powerful tool that accelerates your ability to select and deploy the most effective LLMs for your specific needs. This systematic approach ensures that you remain at the forefront of LLM adoption, driving innovation and efficiency within your organization.
AI Summary
The rapid release of new Large Language Models (LLMs) makes it challenging to keep pace with advancements and determine the best models for specific needs. Public benchmarks, while useful, suffer from a critical flaw: model developers are incentivized to optimize their models for these specific benchmarks, potentially skewing performance results. This article advocates for the creation of internal LLM benchmarks as a more reliable method for evaluation. The core principle is to design benchmarks that test LLMs on tasks or data unique to your organization, thus avoiding the optimization bias present in public benchmarks. The process begins with understanding the motivation: the unreliability of public benchmarks and online opinions due to developer incentives and differing use cases. The article then details how to develop an internal benchmark, emphasizing two key aspects: the task should be uncommon or use proprietary data, and the process should be as automated as possible to allow for frequent testing. Example benchmark types are provided, including those for rare programming languages, internal question-answering chatbots using actual user prompts, and classification tasks with input-output examples. Ensuring automation involves creating a standard interface where only the model integration needs to be updated for new releases. The guide also stresses the importance of testing various models, including both closed-source and open-source options, whenever a significant new release occurs. Regular testing is crucial because models can change over time, even without explicit version changes. A critical section addresses avoiding benchmark contamination, where models might have been pre-trained on the benchmark data if it