The hottest Evaluation Metrics Substack posts right now

And their main takeaways
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Top Technology Topics

In defense of vibes-based evaluations

The AI Frontier • 79 implied HN points • 01 Aug 24
🕹 Technology AI Research Product Development Evaluation Metrics User Experience Data Analysis
  1. Vibes-based evaluations are a helpful starting point for assessing AI quality, especially when specific metrics are hard to define. They allow for initial impressions based on user interactions rather than strict guidelines.
  2. Customers often have unique and unexpected requests that can't easily fit into predefined test sets. Vibes allow for flexibility in understanding real-world usage.
  3. While vibes are useful, they also have downsides, like strong first impressions and limited feedback. A mix of vibes and structured evaluations can provide a better overall understanding of an AI's performance.

But have you considered writing your own evaluation metric?

Mindful Modeler • 299 implied HN points • 21 Nov 23
🕹 Technology Machine Learning Evaluation Metrics Customization Domain Knowledge Model optimization
  1. Consider writing your own evaluation metric in machine learning to better align with your specific goals and domain knowledge.
  2. Off-the-shelf metrics like mean squared error come with assumptions that may not always fit your model's needs, so customizing metrics can be beneficial.
  3. Communication with domain experts and incorporating domain knowledge into evaluation metrics can lead to more effective model performance assessments.

Can we do better than ChatGPT for translation?

Dubverse Black • 98 implied HN points • 05 Jul 23
🕹 Technology Machine Translation Language Models Evaluation Metrics Open Source
  1. The ChatGPT-powered translations are still performing better than other models for most translations.
  2. COMET is an important metric for evaluating translations, focusing on fluency, adequacy, and meaning conveyed.
  3. Open source LLMs like IndicTrans2 and NLLB may be inferior to GCP and GPT, but they can be fine-tuned for better performance.

A brief history of speech to text + how it actually works

Mythical AI • 19 implied HN points • 08 Mar 23
🕹 Technology AI Models Neural Networks Machine Learning Evaluation Metrics
  1. Speech to text technology has a long history of development, evolving from early systems in the 1950s to today's advanced AI models.
  2. The process of converting speech to text involves recording audio, breaking it down into sound chunks, and using algorithms to predict words from those chunks.
  3. Speech to text models are evaluated based on metrics like Word Error Rate (WER), Perplexity, and Word Confusion Networks (WCNs) to measure accuracy and performance.

Langchain’s built-in eval metrics for AI output: how are they different?

AI Encoder: Parsing Signal from Hype • 0 implied HN points • 22 May 24
🕹 Technology AI Evaluation Metrics Machine Learning Bias
  1. Users prefer coherent responses over detailed ones for helpfulness, highlighting the importance of logical structuring in AI output.
  2. Controversial content can be associated with criminality, suggesting that engaging material may overlap with unlawful topics.
  3. Bias from model choices, like using GPT-3.5 Turbo, can impact metric correlations, emphasizing the need for acknowledging biases in AI evaluation.
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