Eval provides a framework and leaderboard benchmarking different language models against a standardized set of tests measuring capabilities like reasoning, knowledge, and fluency.
Eval focuses on benchmarking the core model quality - it is not a full testing solution for downstream applications and interfaces built on top of models.
The standard tests measure narrow academic metrics. Real-world applications require validation of business logic, data correctness, personalized performance etc.
Eval ranks public reference models. It does not offer environment to build custom tests for proprietary systems.
So in summary - OpenAI Eval pushes forward language model foundations, but does not directly solve the need for an automated testing platform tailored for teams building real-world applications leveraging LLMs. Testing application-specific logic, not just the generic model quality, is where the key pain point and opportunity still lies.
Eval is leading the charge on foundational metrics. To fully deliver on real-world quality and reliability as AI proliferates, full stack testing tools like Orangepro will serve a critical role as a complement to Eval's core model benchmarks. So the problem is certainly not yet "solved" from an applied perspective.
There are two key methods discussed for evaluating large language model (LLM) outputs when there may not be one "correct" response:
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Using Rubrics Write a rubric specifying guidelines and criteria to assess different quality dimensions of the LLM output. Then programmatically check if LLM responses meet defined rubric criteria. Allows grading subjective free-form answers.
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Comparing to Expert Response Have human experts provide high quality "ideal" responses for sample inputs. Use another LLM to compare new LLM outputs against the expert provided ones. Defines scale assessing factual consistency, errors, deviations between responses. The passage emphasizes that rigorous evaluation is key during LLM app development and production monitoring.
It recommends using the most advanced LLM available for testing (e.g. GPT-4 vs GPT-3.5) to allow robust analysis.
Also highlights OpenAI's Eval framework with standardized test suites as a resource for benchmarking.
Overall, structured rubrics and comparisons to ideal response benchmarks are two strategies outlined for evaluating free-form LLM outputs with variety of potentially acceptable responses.
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Automated Test Case Generation: LLMs could analyze product requirements, specs and past tests to automatically generate additional test cases covering new scenarios, workflows, data parameters etc. This amplifies test coverage.
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Response Evaluation: Rubrics and decision rules can be programmed so LLMs can automatically grade new application responses for qualities like relevance, accuracy, intent alignment. Enables self-assessment.
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Comparative Analysis: LLMs can cross-check app outputs against expert human responses to identify factual consistency, errors that may have been missed otherwise. Allows benchmarking.
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Monitoring & Compliance: Extensive logging and audit trails of all tests and results generated can provide traceability for model governance and oversight.
In summary, LLMs are uniquely suited to automate repetitive testing workflows, programmatically assess objective and subjective outcomes, and uncover deviations through comparative analysis.
LLMs provide the scale and language understanding essential to thoroughly test complex, real world AI applications. They can complement benchmark tools like OpenAI Eval focused on core model performance with additional validation on downstream business logic, data pipelines etc.
https://github.com/openai/evals https://learn.deeplearning.ai/chatgpt-building-system/lesson/10/evaluation-part-ii https://www.cs.princeton.edu/~arvindn/talks/evaluating_llms_minefield/ https://www.braintrustdata.com/docs