Tracing Knowledge in Language Models Back to the Training Data

Neural language models (LMs) have been shown to memorize a great deal of factual knowledge. But when an LM generates an assertion, it is often difficult to determine where it learned this information and whether it is true. In this paper, we introduce a new benchmark for fact tracing: tracing language models' assertions back to the training examples that provided evidence for those predictions. Prior work has suggested that dataset-level influence methods might offer an effective framework for tracing predictions back to training data. However, such methods have not been evaluated for fact tracing, and researchers primarily have studied them through qualitative analysis or as a data cleaning technique for classification/regression tasks. We present the first experiments that evaluate influence methods for fact tracing, using well-understood information retrieval (IR) metrics. We compare two popular families of influence methods -- gradient-based and embedding-based -- and show that neither can fact-trace reliably; indeed, both methods fail to outperform an IR baseline (BM25) that does not even access the LM. We explore why this occurs (e.g., gradient saturation) and demonstrate that existing influence methods must be improved significantly before they can reliably attribute factual predictions in LMs.