Scalable Influence and Fact Tracing for Large Language Model Pretraining

ICLR 2025 Conference Submission13120 Authors

Keywords: training data attribution, LLM pretraining, influence functions, factual knowledge

TL;DR: We present a gradient-based influence method that scales to LLM pretraining, and we demonstrate its ability to retrieve examples that influence LLM factual predictions.

Abstract: Training data attribution (TDA) methods aim to attribute model outputs back to specific training examples, and the application of these methods to large language model (LLM) outputs could significantly advance model transparency and data curation. However, it has been challenging to date to apply these methods to the full scale of LLM pretraining. In this paper, we introduce a gradient-based method that works effectively at scale, allowing us to retrieve influential examples for an 8B-parameter language model from a pretraining corpus of over 160B tokens with no need for subsampling or pre-filtering. Our method combines several techniques, including optimizer state correction, a task-specific Hessian approximation, and normalized encodings, which we find to be critical for performance at scale. Our method performs best at identifying examples that *influence* model predictions, but classical, model-agnostic retrieval methods such as BM25 still perform better at finding passages which explicitly contain relevant facts. These results demonstrate a misalignment between factual *attribution* and causal *influence*. With increasing model size and training tokens, we find that influence more closely aligns with attribution. Finally, we examine different types of examples identified as influential by our method, finding that while many directly entail a particular fact, others support the same output by reinforcing priors on relation types, common entities, and names.

Primary Area: interpretability and explainable AI

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Submission Number: 13120