𝐇𝐚𝐯𝐞 𝐲𝐨𝐮 𝐞𝐯𝐞𝐫 𝐬𝐮𝐬𝐩𝐞𝐜𝐭𝐞𝐝 𝐭𝐡𝐚𝐭 𝐚𝐧 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐲𝐨𝐮 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐡𝐞𝐚𝐫? | Anna E. Molosky

In a recent study,¹ researchers at Stanford University uncovered that over 58% of responses from the most widely used large language models (LLMs) exhibit sycophancy: providing responses that agree excessively with the user rather than providing truthful or objective insights. If left unaddressed, AI sycophancy can lead you and your teams to make flawed decisions based on biased information, ultimately impacting your organization’s credibility and competitive advantage. 

Here’s how 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐩𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐭𝐚𝐜𝐤𝐥𝐞 𝐀𝐈 𝐬𝐲𝐜𝐨𝐩𝐡𝐚𝐧𝐜𝐲:

𝐖𝐡𝐞𝐧 𝐜𝐫𝐚𝐟𝐭𝐢𝐧𝐠 𝐋𝐋𝐌 𝐩𝐫𝐨𝐦𝐩𝐭𝐬:

◾ Explicitly request unbiased, evidence-based information and ask the LLM to cite its sources—be sure to 𝘷𝘦𝘳𝘪𝘧𝘺 the accuracy of the cited sources

◾ Provide detailed context and 𝘦𝘹𝘱𝘭𝘪𝘤𝘪𝘵𝘭𝘺 ask for transparent reasoning

◾ Request alternative viewpoints to encourage balanced outputs

𝐖𝐡𝐞𝐧 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐢𝐧𝐠 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬:

◾ Adjust training objectives: Modify reward functions to prioritize factual accuracy over user approval

◾ Continuously refine datasets to include diverse, independent perspectives

◾ Implement delayed feedback loops: integrate extended evaluation cycles and retrospective analyses to reduce bias for short-term user preferences

◾ Regularly validate model outputs against neutral benchmarks, recalibrating models for accuracy whenever necessary

◾ Use adversarial testing scenarios to challenge and minimize biases within the AI model

By addressing LLM sycophancy, you will strengthen stakeholder trust, increase the credibility of your AI-generated recommendations, and enable smarter, data-driven decisions that directly impact business outcomes in financial services, SaaS, cloud computing, and data industries.