There can’t be any information about “truthfulness” encoded in an LLM, because there isn’t a notion of “truthfulness” for a program which has only ever been fed tokens and can only ever regurgitate their statistical correlations. If the program was trained on a thousand data points saying that the capital of Connecticut is Moscow, the model would encode this “truthfulness” information about that fact, despite it being false.
To me the research around solving “hallucination” is a dead end. The models will always hallucinate, and merely reducing the probability that they do so only makes the mistakes more dangerous. The question then becomes “for what purposes (if any) are the models profitable, even if they occasionally hallucinate?” Whoever solves that problem walks away with the market.
To me the research around solving “hallucination” is a dead end. The models will always hallucinate, and merely reducing the probability that they do so only makes the mistakes more dangerous. The question then becomes “for what purposes (if any) are the models profitable, even if they occasionally hallucinate?” Whoever solves that problem walks away with the market.