We present EvalAssist, a framework that simplifies the LLM- as-a-judge workflow. The system provides an online criteria development environment, where users can interactively build, test, and share custom evaluation criteria in a structured and portable format. A library of LLM based evaluators is made available that incorporates various algorithmic innovations such as token-probability based judgement, positional bias checking, and certainty estimation that help to engender trust in the evaluation process. We have computed extensive benchmarks and also deployed the system internally in our organization with several hundreds of users.

User flow diagram for EvalAssist in the direct assessment evaluation, illustrating criteria definition, test data input, annotation, AI evaluator selection, result review, iterative adjustments, and criteria export for dataset-wide evaluation via SDK.

Interactive AI (IAI) systems are increasingly popular as the human-centered AI design paradigm is gaining strong traction. However, evaluating IAI systems, a key step in building such systems, is particularly challenging, as their output highly depends on the performed user actions. Developers often have to rely on limited and mostly qualitative data from ad-hoc user testing to assess and improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. We also describe how we have applied InteractEva to evaluate a commercial IAI system, leading to both quality improvements and better data-driven design decisions.

Evaluating interactive AI (IAI) systems is a challenging task, as their output highly depends on the performed user actions. As a result, developers often depend on limited and mostly qualitative data derived from user testing to improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. InteractEva employs (a) a user simulation backend to test the system against different use cases and user interactions at scale with (b) an interactive frontend allowing developers to perform important quantitative evaluation tasks, including acquiring a performance overview, performing error analysis, and conducting what-if studies. The framework has supported the evaluation and improvement of an industrial IAI text extraction system, results of which will be presented during our demonstration.