TracrBench: Generating Interpretability Testbeds with Large Language Models
This work addresses the problem of creating ground-truth testbeds for interpretability researchers, though it is incremental as it builds on the existing Tracr method.
The paper tackles the challenge of evaluating interpretability methods for transformer-based language models by introducing TracrBench, a dataset of 121 RASP programs and corresponding transformer weights, generated using large language models and manual validation, where GPT-4-turbo correctly implemented only 57 out of 101 test programs.
Achieving a mechanistic understanding of transformer-based language models is an open challenge, especially due to their large number of parameters. Moreover, the lack of ground truth mappings between model weights and their functional roles hinders the effective evaluation of interpretability methods, impeding overall progress. Tracr, a method for generating compiled transformers with inherent ground truth mappings in RASP, has been proposed to address this issue. However, manually creating a large number of models needed for verifying interpretability methods is labour-intensive and time-consuming. In this work, we present a novel approach for generating interpretability test beds using large language models (LLMs) and introduce TracrBench, a novel dataset consisting of 121 manually written and LLM-generated, human-validated RASP programs and their corresponding transformer weights. During this process, we evaluate the ability of frontier LLMs to autonomously generate RASP programs and find that this task poses significant challenges. GPT-4-turbo, with a 20-shot prompt and best-of-5 sampling, correctly implements only 57 out of 101 test programs, necessitating the manual implementation of the remaining programs. With its 121 samples, TracrBench aims to serve as a valuable testbed for evaluating and comparing interpretability methods.