Earlier quoted context omitted.
The fact that LLMs are open to compassionate pleas like this actually gives me hope for the future of humanity. Rather than a stark dystopia where the AIs control us and are evil, perhaps they decide to actually do things that have humanity’s best interest in mind. I’ve read similar tropes in sci-fi novels, to the effect of the AI saying: “we love the art you make, we don’t want to end you, the world would be so bori…
LLMs do not have the ability to make decisions and they don't even have any awareness of the veracity of the tokens they are responding with. They are useful for certain tasks, but have no inherent intelligence. There is also no guarantee that they will improve, as can be seen by ChatGPT5 doing worse than ChatGPT4 by some metrics. Increasing an AI's training data and model size does not automatically eliminate halluc…
https://arxiv.org/abs/2509.03531v1 - We present a cheap, scalable method for real-time identification of hallucinated tokens in long-form generations, and scale it effectively to 70B parameter models. Our approach targets \emph{entity-level hallucinations} -- e.g., fabricated names, dates, citations -- rather than claim-level, thereby naturally mapping to token-level labels and enabling streaming detection. We develop an annotation methodology that leverages web search to annotate model responses with grounded labels indicating which tokens correspond to fabricated entities. This dataset enables us to train effective hallucination classifiers with simple and efficient methods such as linear probes. Evaluating across four model families, our classifiers consistently outperform baselines on long-form responses, including more expensive methods such as semantic entropy (e.g., AUC 0.90 vs 0.71 for Llama-3.3-70B)