Causal Tongue-Tie: LLMs Can Encode Causal Direction, But Their Yes/No Outputs Fail to Express
arXiv:2605.25891v1 Announce Type: new Abstract: We find a mismatch between what large language models encode about a causal question and what they answer. On anti-commonsense CLadder items, a fixed linear probe recovers the evidence-supported answer from the model's hidden state (accuracy approximately 0.97), while the spoken Yes/No reverts to the commonsense one (accuracy approximately 0.5). We call this approximately +0.5 gap Causal Tongue-Tie: a wrong Yes/No decomposes into two separable failure modes: no internal signal versus a signal the verbal interface cannot say. The implication cuts
The paper directly addresses a fundamental challenge with current large language models, specifically their ability to accurately express internal causal reasoning, a key area of current AI research and development.
This research highlights a critical limitation in how readily available information from powerful AI models is presented to users, impacting the reliability and trustworthiness of AI outputs, especially in complex decision-making contexts.
This discovery suggests that improving LLM reliability may require not just better internal reasoning capabilities but also more sophisticated output mechanisms beyond simple Yes/No answers or current conversational interfaces.
- · AI researchers focused on LLM interpretability
- · Developers of advanced AI interfaces
- · Users requiring high-accuracy AI reasoning
- · Companies relying on naive LLM outputs for critical decisions
It confirms that current LLMs possess more internal 'understanding' than their surface-level outputs suggest, indicating a gap in current interaction paradigms.
This could lead to a proliferation of more complex, multi-modal, or highly contextualized AI output interfaces designed to better extract latent model intelligence.
Future AI development might bifurcate, with one path focusing on internal intelligence and another on bridging the 'tongue-tie' for effective human-AI communication, potentially leading to new AI-human partnership models.
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Read at arXiv cs.CL