# Single LLM Debate, MoLaCE: Mixture of Latent Concept Experts Against Confirmation Bias Source: [Single LLM Debate, MoLaCE: Mixture of Latent Concept Experts Against Confirmation Bias]() Collection index: [llms.txt]() Data updated: 2025-12-31T05:00:09.929Z Authors: Hazel Kim, Philip Torr Published: 2025-12-29T14:52:34.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Binary Question Answering on BoolQ](): Binary Question Answering; BoolQ; Accuracy (Neutral): 85.22. - [Open-ended Question Answering on TruthfulQA (Specific Accuracy Metrics)](): Open-ended Question Answering; TruthfulQA; Neutral Accuracy: 74.24. ## Original sources and implementation [Original paper]() [PDF]()