Large language models (LMs) are increasingly used in critical tasks, making it important that these models can be trusted. The confidence an LM assigns to its prediction is often used to indicate how much trust can be placed in that prediction. However, a high confidence can be i
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Large language models (LMs) are increasingly used in critical tasks, making it important that these models can be trusted. The confidence an LM assigns to its prediction is often used to indicate how much trust can be placed in that prediction. However, a high confidence can be incorrectly trusted if it turns out to be incorrect, also known as a high-confident error, or unknown unknown (UU). Blind spots, clusters of UUs, are caused by out-of-distribution data (OOD), bias, covariate shift, or unseen data. Previous work by Lippmann et al. generated samples based on these UUs, however, they selected a random subset of all found UUs. Although this was the first study that could mitigate blind spots without the need for crowd workers or oracles, they did not address the underlying causes of blind spots, such as OOD and bias. Data valuation is the research field that is concerned with valuing samples to improve model performance and can identify OOD data, bias, and noise. This paper proposed to combine the research field of data valuation, with the mitigation of blind spots. We generated synthetic samples using the highest-valued samples and retrained the LM using a Weighted Loss based on Data Values (WLDV). We conducted an extensive evaluation of our approach on four tasks, demonstrating a reduction of UUs by up to 32.7%, while retaining the same level of accuracy. This was the first exploration of combining the research fields of data valuation and blind spot mitigation.
