Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning
This work addresses the problem of understanding evidence usage in AI models for researchers, showing incremental insights into error attribution.
The study analyzed the relationship between evidence retrieval and interpretation errors in large language models for few-shot learning, finding a strong link between prediction and retrieval errors but not between retrieval and interpretation errors.
A key aspect of alignment is the proper use of within-document evidence to construct document-level decisions. We analyze the relationship between the retrieval and interpretation of within-document evidence for large language model in a few-shot setting. Specifically, we measure the extent to which model prediction errors are associated with evidence retrieval errors with respect to gold-standard human-annotated extractive evidence for five datasets, using two popular closed proprietary models. We perform two ablation studies to investigate when both label prediction and evidence retrieval errors can be attributed to qualities of the relevant evidence. We find that there is a strong empirical relationship between model prediction and evidence retrieval error, but that evidence retrieval error is mostly not associated with evidence interpretation error--a hopeful sign for downstream applications built on this mechanism.