The Base-Rate Effect on LLM Benchmark Performance: Disambiguating Test-Taking Strategies from Benchmark Performance (2024)

Abstract: Cloze testing is a common method for measuring the behavior of large language models on a number of benchmark tasks. Using the MMLU dataset, we show that the base-rate probability (BRP) differences across answer tokens are significant and affect task performance ie. guess A if uncertain. We find that counterfactual prompting does sufficiently mitigate the BRP effect. The BRP effect is found to have a similar effect to test taking strategies employed by humans leading to the conflation of task performance and test-taking ability. We propose the Nvr-X-MMLU task, a variation of MMLU, which helps to disambiguate test-taking ability from task performance and reports the latter.

## introduction benchmarking has become an ubiquitous practice in machine learning. ideally, these benchmarks provide human-interpretable measures of context specific abilities @xcite , however, as standardized tests, benchmarks may be susceptible to response strategies that skew the reported metrics and belie their utility @xcite . in the context of large language models (llms), many benchmarks are measured by way of cloze testing @xcite , with the most probable allowed completion considered to be the model's preferred answer. we suspect that the identified preference of many models may be undesirably biased by the independent intrinsic probabilities associated with each completion option, referred to as the base-rate probability (brp) effect. each method measures the probability of each token measured given the shared context and the method-specific contexts. cloze prompting uses a single method-specific context and measures the probability of multiple candidate tokens. cf prompting uses a different method-specific context for each candidate token and measures the probabilities of the same canary token. @xcite argues that llms perform too well on mmlu and propose a set of questions which require higher level reasoning. our work finds differently that zero shot performance on the nvr-x-mmlu test, with no changes to the questions, remains a challenge for all tested models. @xcite propose rewording perturbation of prompts in numerous benchmarks, including mmlu, to improve robustness of results. this addresses a deficiency in benchmark results, but not the brp effect specifically addressed here. concurrent to our work, @xcite proposes techniques for improving performance on tasks that are susceptible to similar base rate effects. our proposed method differs in that it addresses the effect at task level rather than strategy level. ## experimental design & results here we present the experiments used to evaluate the hypotheses in ## discusssion in this paper, we investigated the efficacy of cf prompting to mitigate base-rate biases, using the mmlu benchmark as a testing ground. as expected, we found that brp disparities between completion tokens have a direct effect on model behavior, including factual accuracy. the same, however, was also surprisingly true when using cf prompting. we then propose a simple variation on mmlu, dubbed nvr-x-mmlu, that identifies and controls for brp effects and some superficial heuristics resulting in a more meaningful metric. this study addresses only a small selection of simple test-taking heuristics that a model might employ. future work can investigate whether other known test-taking heuristics seen in humans (e.g. answer length, sequential runs of the same answer, numeric outliers, etc.) are also present in llm behavior. failure of hypothesis (h3), combined with positive results for (h1) and (h2), reinforces the need for methods of controlling for undesired brp effects in model behavior.