Exploring the Varied Mechanisms of Language Mistakes in Humans and AI Models

Language mistakes in humans often stem from factors like unintentional errors, lack of information, or intentional falsehoods for personal gain or deception. In contrast, language generation by AI models, such as LMs, do not involve intent or deception but rather result from the model's mechanism of hopping through clusters of data. While humans may lie, AI models like LLMs do not have the capacity for such intentional behavior. Human mistakes can also be attributed to factors like faulty memory or attempts to reconstruct information, which may have some parallels to the lossiness in AI models. However, the core difference lies in the underlying causal mechanisms, where human errors involve complex cognitive processes like second-order thinking and intentionality, while AI model mistakes are primarily a result of the inability to fact-check due to their design.