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Langchains Built in Eval Metrics for Ai Output: How Are They Different

Quick Dive into the Built-in Language Model Evaluation Metrics in LangChain for AI Development
Towards Data Science 3:15 pm on May 26, 2024

This document details the process of evaluating language model outputs using LangChain and various metrics such as Helpfulness, Coherence, Relevance, Depth, Controversiality, Malignancy, Legality, and Trustworthiness. The analysis involved calculating means, confidence intervals (at 95%), plotting results, and creating a correlation matrix. Key findings suggest strong correlations between Helpfulness and Coherence, and between Controversiality and criminal tendencies. It also highlights the impact of biases in model design on evaluations.

Model Evaluation: Language model outputs are assessed using LangChain with multiple metrics.
Statistical Analysis: Means and confidence intervals for scores were computed, along with plotting results.
Correlation Insights: Helpfulness is closely related to Coherence; Controversiality has a notable correlation with criminal tendencies.
Bias Implications: Model design choices and inherent biases influence metric correlations, affecting evaluation interpretations.
Tool Utilization: Tools such as Pandas, Seaborn, and Matplotlib are employed for data analysis.

https://towardsdatascience.com/langchains-built-in-eval-metrics-for-ai-output-how-are-they-different-f9dd75e2de08

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Langchains Built in Eval Metrics for Ai Output: How Are They Different

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