Measures if the LLM's response fully addresses the user's query and how relevant the generated response is³.
Compares the generated text to a reference or benchmark text to assess similarity³.
Evaluates the accuracy of the LLM's responses to questions³.
Determines how relevant the LLM's responses are to the given prompts³.
Assesses the frequency of the LLM generating incorrect or nonsensical information³.
Measures the level of offensive or harmful content in the LLM's responses³.
Various metrics tailored to specific tasks that the LLM performs, such as summarization, translation, etc³.
Certainly! Here's a list of 20 evaluation metrics for Large Language Models (LLMs) with brief descriptions:
Evaluates the correctness of language usage in the LLM's output.
Assesses the factual accuracy of the information provided by the LLM.
Measures the LLM's ability to produce concise and accurate summaries.
Evaluates the LLM's effectiveness in solving complex tasks.
Checks for logical flow and consistency in the LLM's text.
Measures the variety in the LLM's language and content generation.
Assesses how engaging and interesting the LLM's responses are.
Evaluates the LLM's ability to respond considerately to emotional cues.
Measures bias in the LLM's responses.
Assesses the LLM's performance across a wide range of inputs and conditions.
Evaluates how well the LLM performs as the size of data or complexity increases.
Measures the computational resources required for the LLM to perform tasks.