Enhancing LLM Reliability: Detecting Confabulations with Semantic Entropy
How do "confabulations" differ from other LLM errors?
Confabulations are a specific type of error in LLMs where the model generates arbitrary and incorrect responses to identical queries, often due to subtle variations in the input or random seed. This is distinct from errors caused by training on faulty data or systematic reasoning failures.
What new method did Oxford's OATML group develop?
Oxford's OATML group developed a statistical approach using entropy-based uncertainty estimators to detect "confabulations," a type of error in large language models (LLMs) where they generate arbitrary and incorrect responses. The method, called "semantic entropy," focuses on the meaning rather than the exact wording of responses, and can identify when LLMs are likely to produce unreliable outputs.
What are "hallucinations" in the context of LLMs?
"Hallucinations" in the context of LLMs refer to the generation of plausible-sounding but factually incorrect or nonsensical information. This phenomenon occurs when the model, despite its impressive language skills, fails to accurately represent or reason about the real world.