Gramener CEO Anand S Warns of Risks in Blindly Trusting LLMs, Advocates for Model Literacy
In Brief
In a conversation with Metaverse Post – Anand S, CEO of Gramener highlighted the potential pitfalls of excessive reliance on LLMs.
With hundreds of AI and ML tools saturating the market, the innovation potential is limitless. Numerous startups are emerging to tackle industry challenges, while a constant influx of enhanced Large Language Models each week amplifies the transformative power of generative AI. This technological convergence is reshaping industries with boundless possibilities.
However, the release and widespread use of powerful language models like Gemini raises important ethical and practical considerations, and asks us to consider a question – Can we as humans blindly trust such models?
In a conversation with Metaverse Post — Anand S, CEO of US-based B2B SaaS company Gramener emphasized the potential pitfalls of excessive reliance on LLMs like ChatGPT, cautioning that this trend is likely to persist with emerging models such as Gemini.
“Even with the right training data, a human may make errors when going outside their area of expertise. Is a large language model therefore extrapolating, or is it recollecting from what it has learned? There is a huge difference, and it’s worth testing. We build confidence through intuitions with repeated interactions with people,” said Anand. “We know we need to probe to investigate a stranger and that is how we probably should interact with large language models as well.”
For instance, in the Varghese versus Southern Airlines company limited case, a lawyer cited fictional ChatGPT-generated cases, leading to a judge penalizing the lawyer, emphasizing the risks of uncritical use. Anand highlights the need for caution and scrutiny when leveraging such language models.
Further to this, the field calls for a robust feedback mechanism. ChatGPT encourages user feedback through a simple thumbs up/thumbs down button, coupled with text input. This approach gathers valuable insights into what works well and what doesn’t. This feedback system is likely to become more common with the use of large language models, helping improve their performance.
“It’s not very economical to monitor every single output of an LLM, but being able to flag off potential errors is a powerful lever,” Anand told Metaverse Post.
“What’s interesting is that large language models themselves are pretty good at assessing other large language models. So rather than use a human exclusively for monitoring the output, we can use a combination of LLMs and humans, with LLMs eventually taking over the bulk of the human role as they evolve,” he added.
Model Literacy is the Way Forward
In the realm of LLMs, a key principle emerges – the more interaction, the deeper the understanding. This straightforward concept underscores the relationship between user engagement and comprehension of LLM capabilities, ultimately driving increased utilization.
“It’s also about understanding what a particular LLM is good for. For example, I wouldn’t use DALL-E to generate logos because it doesn’t generate text very well. But it does a great job of generating logo ideas and logo designs. This is an example of model literacy that’s come up by repeated usage and, therefore, gives me defined areas of trust within which I can use the model more effectively,” said Anand.
In the quest for a balanced integration of language models like Gemini into our technological landscape, Anand suggests several strategies. Firstly, he advocates for daily interactions with large language models, fostering a habit that builds over time. Likewise, ensuring universal access to these tools is crucial, with a call for more encouragement in personal usage. Anand notes the existing trend of organizations blocking ChatGPT on work laptops, underscoring the need for supportive communication.
He proposes implementing organizational mechanisms to provide access and encourage experimentation with Language Models (LLMs) within an environment.
“Ultimately, it’s when people get familiar that they will understand what to trust, when to use it, and how to use it. Increased usage and encouraging increased usage, are the most important strategy and educational initiatives anyone can employ,” Anand told Metaverse Post.
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About The Author
Kumar is an experienced Tech Journalist with a specialization in the dynamic intersections of AI/ML, marketing technology, and emerging fields such as crypto, blockchain, and NFTs. With over 3 years of experience in the industry, Kumar has established a proven track record in crafting compelling narratives, conducting insightful interviews, and delivering comprehensive insights. Kumar's expertise lies in producing high-impact content, including articles, reports, and research publications for prominent industry platforms. With a unique skill set that combines technical knowledge and storytelling, Kumar excels at communicating complex technological concepts to diverse audiences in a clear and engaging manner.
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Kumar is an experienced Tech Journalist with a specialization in the dynamic intersections of AI/ML, marketing technology, and emerging fields such as crypto, blockchain, and NFTs. With over 3 years of experience in the industry, Kumar has established a proven track record in crafting compelling narratives, conducting insightful interviews, and delivering comprehensive insights. Kumar's expertise lies in producing high-impact content, including articles, reports, and research publications for prominent industry platforms. With a unique skill set that combines technical knowledge and storytelling, Kumar excels at communicating complex technological concepts to diverse audiences in a clear and engaging manner.
