Single LLM Debate, MoLaCE: Mixture of Latent Concept Experts Against Confirmation Bias

TL;DR

This paper introduces MoLaCE, a lightweight framework that mitigates confirmation bias in large language models by dynamically mixing latent concept experts, enhancing robustness and debate capabilities efficiently.

Contribution

MoLaCE is a novel, inference-time method that dynamically reweights latent concepts to reduce confirmation bias in LLMs, applicable to single and multi-agent debate settings.

Findings

Reduces confirmation bias across various prompts.

Improves robustness and factual correctness.

Matches or surpasses multi-agent debate performance with less computation.

Abstract

Large language models (LLMs) are highly vulnerable to input confirmation bias. When a prompt implies a preferred answer, models often reinforce that bias rather than explore alternatives. This phenomenon remains underexplored, yet it is already harmful in base models and poses an even greater risk in multi-agent debate, where echo chambers reinforce bias instead of correction. We introduce Mixture of Latent Concept Experts (MoLaCE), a lightweight inference-time framework that addresses confirmation bias by mixing experts instantiated as different activation strengths over latent concepts that shape model responses. Our key insight is that, due to the compositional nature of language, differently phrased prompts reweight latent concepts in prompt-specific ways that affect factual correctness, so no single fixed intervention can be applied universally across inputs. This design enables a single LLM to emulate the benefits of debate internally while remaining computationally efficient and scalable. It can also be integrated into multi-agent debate frameworks to diversify perspectives and reduce correlated errors. We empirically show that it consistently reduces confirmation bias, improves robustness, and matches or surpasses multi-agent debate while requiring only a fraction of the computation.