LLM Application Evaluation Task

An LLM Application Evaluation Task is an AI application evaluation for LLM-based applications.

• Context:
  • It can (typically) involve evaluating an LLM's ability to perform specific tasks, such as text classification or sequence generation, against predefined datasets.
  • It can (typically) use different types of evaluators, such as correctness or summary evaluators, to score the LLM's output against expected results.
  • It can (often) rely on a pre-configured LangSmith Dataset Management system to organize and manage datasets used in the evaluation process.
  • It can (often) include feedback loops where human reviewers provide input to assess and improve LLM performance.
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  • It can range from simple evaluations (e.g., binary classification) to more complex metrics (e.g., F1 scores, precision, and recall) that measure task accuracy.
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  • It can be supported by an LLM Application Evaluation System (based on an LLM app evaluation framework).
  • It can involve tracing LLM outputs to capture inputs and track the pipeline's intermediate steps for better diagnostics.
  • It can be part of an ongoing evaluation strategy where LLMs are repeatedly tested across multiple dataset versions or subsets.
  • It can allow evaluation on subsets of data or dataset splits to fine-tune model behavior in specific contexts or use cases.
  • It can be used in conjunction with multiple evaluators to generate various performance metrics (e.g., precision, recall, and F1 scores) for better insight.
  • It can support advanced use cases such as evaluating on intermediate steps of the LLM pipeline or running repeated evaluations to reduce noise.
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• Example(s):
  • Evaluating an LLM pipeline for Toxic Content Detection using a dataset with toxic and non-toxic examples.
  • Running an evaluation on a dataset version to track changes and improvements in LLM performance.
  • Using multiple evaluators to assess a classification task's precision, recall, and F1 score.
  • Evaluating an LLM application using dataset splits (e.g., training and test data) to better understand performance across different scenarios.
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• Counter-Example(s):
  • Simple Accuracy Testing focuses on measuring only the final output of a model without tracing intermediate steps or collecting detailed feedback.
  • Manual Model Evaluation involves a human manually reviewing model output without automated evaluators or scoring functions.
  • Single Metric Evaluation that only provides a single score (e.g., accuracy) instead of multiple metrics like precision and recall.
  • Basic Dataset Testing lacks advanced features like feedback collection, dataset versioning, or repeated evaluations used in a more comprehensive evaluation system.
• See: LangSmith Evaluation Framework, LLM Performance Scoring, LangSmith Tracing, LLM Feedback Mechanism.

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