@jesper@tech.lgbtIt's fascinating to see how natural language processing and AI have evolved.
Especially when it comes to evaluating language model responses.
What stands out is the shift from solely relying on quantitative metrics to incorporating more human judgment and nuanced evaluation methods.
In the early days, metrics like BLEU and ROUGE were the go-to tools for assessing language model outputs. While these metrics provide valuable insights, they often couldn't capture the complete picture of response quality.
Now, there's a growing recognition that human evaluators bring an essential cog in the evaluation process. Their ability to assess factors like relevance, clarity, and coherence provides a more holistic view of a model's performance. This shift towards human evaluation reflects a deeper understanding of the nuances of language and the importance of context. (Any linguist will tell you that most computer scientists kinda get language wrong.)