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Episode 53 – Language Biases in AI with Greg Bennett Pt. 2
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Discover the hidden biases in large language models and how they skew towards English, creating unique challenges for other languages when it comes to artificial intelligence (AI). In this episode, Greg Bennett, head of prompt engineering at ZEALS, dissects the influence of training data predominantly sourced from English materials and the subsequent effectiveness of English prompts. We reveal how tiny linguistic tools called discourse markers can significantly enhance conversational flow in AI-generated responses, especially when navigating non-English languages. Linguistics professionals play a critical role in prompt engineering; their expertise in language structures and sociolinguistics gives them an edge in creating natural and engaging AI outputs. Hear about the intriguing differences between OpenAI and Anthropic's language models and how each handles discourse markers. We discuss why Anthropic's models seem to have an inherent grasp of these markers, whereas OpenAI's models need explicit instructions.
Reflecting on the evolution of conversational AI, we go back to the early days of voice assistants like Cortana in 2014, sharing personal experiences and challenges faced in those formative years. Fast forward to the present and we illustrate how advancements in generative AI have transformed conversational UX. Learn how specifying discourse formats, whether for articles or chat messages, can guide AI to produce the desired style and register, making interactions smoother and more intuitive.
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