Fine-tuning LLMs on observational logs can amplify spurious patterns such as weekday effects, and a confounder-subtraction method, DeconfoundLM, restores causal signal in self-built simulations.
Using advanced llms to enhance smaller llms: An interpretable knowledge distillation approach
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Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
Fine-tuning LLMs on observational logs can amplify spurious patterns such as weekday effects, and a confounder-subtraction method, DeconfoundLM, restores causal signal in self-built simulations.