Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
This provides a diagnostic tool for AI safety researchers and practitioners to understand and mitigate risks in LLMs, though it is incremental as it builds on existing attribution methods.
The paper tackles the problem of diagnosing undesirable behaviors in Large Language Models, such as harmful content generation and biases, by introducing a framework that analyzes representation gradients in activation space, achieving precise sample-level and token-level attribution for tasks like tracking harmful content and detecting backdoor poisoning.
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking outputs to their training data. We systematically evaluate our method for tasks that include tracking harmful content, detecting backdoor poisoning, and identifying knowledge contamination. The results demonstrate that our approach not only excels at sample-level attribution but also enables fine-grained token-level analysis, precisely identifying the specific samples and phrases that causally influence model behavior. This work provides a powerful diagnostic tool to understand, audit, and ultimately mitigate the risks associated with LLMs. The code is available at https://github.com/plumprc/RepT.