#llms

#llms

MS MARCO Web Search dataset consists of a high quality set of web pages that mirrors the highly-skewed web document distribution, a query set that reflects the real web query distribution.

Building LLM-powered applications is comparable to developing modern cloud-native applications, requiring notable support for REST and RPC protocols, where Go excels.

I wanted to always understand what customers are doing, what are their environments running. One of the things at QCon, I found at the time that the hottest topic in 2018 and 2019 frame, number one is Java.

gander's interface asks if you want to replace selected code or put its suggestions either before or after your selection.

To evaluate LLMs' reasoning capabilities, we curated seven algorithmic reasoning tasks from Big-Bench Hard designed to measure step-by-step reasoning in zero-shot settings.

The maturity of AI remains a debated topic, with skepticism about LLMs utility beyond chatbots and concerns about AGI risk.

Building applications utilizing large language models (LLMs) poses challenges for developers, particularly due to the lack of educational resources akin to those available for system design.

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.