The integration of LLMs with voice capabilities provides a transformative mechanism for personalized customer interactions, enhancing engagement and satisfaction through dynamic, two-way voice communication.
"I've been doing AI for 15 years, people, I'm not just spouting sh** - I'm not Sam Altman, okay?" Fadell expressed his deep-rooted experience in AI development, emphasizing a significant understanding beyond the mainstream AI discourse.
In our research, we find evidence that the effect of CoT fundamentally depends on generating sequences of words that increase the probability of the correct answer when conditioned upon. Interestingly, our findings suggest that CoT can succeed even in the face of invalid demonstrations, opening up new discussions about the interplay of reasoning and memorization in LLM outputs.
The testing on different downstream tasks, including fine-tuning and quantization, shows that while fine-tuning can improve task effectiveness, it can simultaneously increase jailbreaking vulnerabilities in LLMs.