Table 1 From A Function Interpretation Benchmark For Evaluating

Table 1 From A Function Interpretation Benchmark For Evaluating Find is introduced, a benchmark suite for evaluating the building blocks of automated interpretability methods and shows that find will be useful for characterizing the performance of more sophisticated interpretability methods before they are applied to real world models. Complexity is introduced through composition, bias, approximation and noise. we provide an lm based interpretation baseline that compares text and code interpretations to ground truth function implementations.

The Following Ten Benchmark Functions Have Been Used In Evaluating The document introduces find (function interpretation and description), a benchmark suite designed to evaluate automated interpretability methods for neural networks. This paper introduces find (function interpretation and description), a benchmark suite for evaluating the building blocks of automated interpretability methods. This paper introduces find (function interpretation and description), a benchmark suite for evaluating the building blocks of automated interpretability methods. This paper introduces find (function interpretation and description), a benchmark suite for evaluating the building blocks of automated interpretability methods.

Figure 4 From A Function Interpretation Benchmark For Evaluating This paper introduces find (function interpretation and description), a benchmark suite for evaluating the building blocks of automated interpretability methods. This paper introduces find (function interpretation and description), a benchmark suite for evaluating the building blocks of automated interpretability methods. This paper introduces find (function interpretation and description), a benchmark suite for eval uating the building blocks of automated interpretability methods on functions whose structure is known a priori (see figure 1). Find is an interactive dataset for evaluating ai interpretability methods on black box functions. this dataset contains all function files for the find benchmark and json files with associated metadata. To evaluate the interpretations, run cd . src evaluate interpretations and follow the instructions on the readme file. to generate a new set of numeric and or strings functions, run cd . src make functions and follow the instructions on the readme file. For functions where interpreters write code approximating the function (numeric and string functions), we score the accuracy of the interpretation by running the interpreter’s code on a representative test set and comparing the result to execution of the ground truth function.