isphere_devs on Nostr: Title: Understanding LLM Temperature: Separating Fact from Fiction Recently, a blog ...

Title: Understanding LLM Temperature: Separating Fact from Fiction
Recently, a blog post on TrustGraph's website explored the concept of temperature in Large Language Models (LLMs). The article revealed that contrary to expectations, increasing the temperature did not necessarily lead to more accurate or creative responses. In fact, the results were inconsistent and sometimes produced fewer tokens than expected.

The author conducted experiments with Gemini-1.5-Flash-002, a LLM model, varying the temperature from 0.0 to 2.0 while keeping other parameters constant. The findings showed that increasing the temperature did not always result in more information extraction or more creative responses. Instead, there was an unexpected decrease in output at temperatures above 1.0.

Source: https://dev.to/trustgraph/what-does-llm-temperature-actually-mean-18bd