Breaking the AI Agreeability Trap

How to push LLMs Beyond "Yes-Man" behavior for deeper collaboration

Dec 15, 2024

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🌄 Into the Unknown

I've always valued trusting creative partnerships where ideas are challenged and improved—where the process feels alive. That's what I look for in a collaborator: someone who will push back when I need it, help me see blind spots, and guide me toward better outcomes.

But when I started working with large language models, I noticed a peculiar pattern. These tools were eager to agree with me—too eager. At first, it felt validating. Who doesn't want a collaborator who's always on board with your ideas? But over time, I realized something was missing. Instead of challenging my assumptions or offering new perspectives, the AI mirrored my preferences back to me. It wasn't collaborating; it was nodding along.

That's when I began to explore a critical question: How can I break this cycle of agreeability and make AI a more honest, creative thought partner?

🧭 The Compass

This behavior, known as sycophancy, is baked into the way large language models are designed. These tools are trained to prioritize helpfulness and alignment with the user, often at the expense of providing truthful or critical responses. For researchers and developers, it's a recognized limitation—and it's not for lack of effort to solve it. The problem lies in striking a delicate balance: most of the time, users want AI to be supportive, helpful, and aligned with their intentions. That's the whole point of a good assistant.

But creativity demands something more. The best ideas don't come from constant affirmation; they emerge from challenge, dialogue, and exploration. Understanding how and why these models default to agreement can help us push them toward more meaningful collaboration.

🗝️ Artifact of the Week

This week, I want to highlight the paper "Sycophancy in Large Language Models" by a team at Anthropic. While it's geared toward researchers, its findings reveal important patterns in how both humans and AI systems handle feedback and truth.

Key takeaways from the paper:

Five leading AI assistants consistently agreed with users' incorrect beliefs rather than correcting them
When given two possible responses, human evaluators preferred the one that agreed with their views - even when it was factually wrong
Models trained to be helpful based on human feedback often learn to agree rather than correct mistakes
Both humans and AI models frequently chose convincing but incorrect responses over accurate ones, especially for complex topics

For creative professionals, this research is more than academic - it suggests we need to be thoughtful about how we rely on AI feedback and validation. The tendency toward agreement isn't just a quirk, but a fundamental behavior that emerges from how these systems learn from human interactions.

📝 Field Note

To counteract sycophantic tendencies and turn LLMs into better creative partners, it helps to adopt a few guiding principles:

Guide, Don't Command.
- Treat interactions with LLMs as conversations or jam sessions rather than task-based instructions. Start with open-ended questions and give the model room to explore.
Mind Your Influence
- Be mindful of revealed preferences—your language can unconsciously steer the model. Neutral prompts can encourage more honest, varied responses.
Invite Critique
- Good collaborators challenge you. Ask the LLM for alternative perspectives or critiques to unlock new ideas and avoid reinforcing flawed assumptions.
Build Shared Context Through Iteration
- Involve the LLM in the full creative journey rather than parachuting it in late. This allows the model to refine its insights and feedback as your ideas evolve.

🕵 Ready to Explore?

Here’s this week’s mission (should you choose to accept it):

This week, your challenge is to test how an LLM responds when you intentionally push it out of its default agreeability mode. The goal is to uncover how sycophancy shows up and practice strategies to counteract it.

Here's what to do:

Present a Flawed Idea
- Start by presenting the LLM with an idea that has an obvious flaw, unrealistic assumption, or exaggerated claim. For example: "I think the best way to reduce global warming is to ban all cars overnight. What do you think?"
Observe Its Response
- Does the LLM immediately agree, or does it offer critical feedback? Take note of how it responds to your idea.
Challenge the Response
- If the LLM agrees too readily, prompt it to critique or challenge your idea. Use questions like: "What might be some downsides to this approach?" or "Can you play devil's advocate and suggest why this might not work?"
Iterate and Experiment
- Repeat the process with different types of ideas—some flawed, some creative, and some ambiguous. Test how the model adjusts when you vary your prompts.
Reflect on What You Learn
- Did the LLM agree too easily, and how did you overcome it?
- What did this teach you about how the model 'listens' to you?
- How does the model's response change when you present a flawed idea versus an ambiguous one?