Study Finds Context Attribution Methods Unreliable When LLMs' Training Data Overlaps with Input
A recent arXiv preprint introduces new evaluation metrics and a benchmark to test how well context attribution methods in large language models (LLMs) work when input context overlaps with the models' training data. The study finds that current attribution techniques struggle to distinguish between information learned during training and information provided in the prompt, leading to unreliable attribution scores in these scenarios.
Why it matters: This finding exposes a key limitation in widely used tools for interpreting LLM behavior, raising concerns for their use in critical applications where understanding model reasoning is essential.
Full story at: arXiv Computation and Language ↗