Option Symbol Matters: Investigating and Mitigating Multiple-Choice Option Symbol Bias of Large Language Models (2025)

Abstract: Multiple-Choice Question Answering (MCQA) is a widely used task in the evaluation of Large Language Models (LLMs). In this work, we reveal that current LLMs’ performance in MCQA could be heavily influenced by the choice of option symbol sets, due to the option symbol bias. That is, when altering only the option symbols (e.g., A/B/C/D \rightarrow i/ii/iii/iv), the results could vary sharply, leading to a margin of approximately 10% in accuracy. To uncover the mechanisms behind this, we investigate the internal components of LLMs from a causal perspective. By measuring the causal effects, we identify a small subset of attention heads responsible for the symbol bias. Subsequently, we interpret these key components in a human-understandable way, showing that attention heads with higher causal effects are more likely to focus on only option symbols, while those with lower causal effects tend to distribute their attention across the content of questions and options. It also motivates us to pursue debiasing based on the causal effects. Specifically, to mitigate such bias, we propose a tuning-free, causal effect driven debiasing method which intervenes the activations of identified components according to their causal effects, with stronger interventions corresponding to higher causal effects. Experimental results demonstrate that the proposed method not only alleviates aforementioned bias, but also improves the MCQA performance of LLMs.

## introduction multiple-choice question answering (mcqa) is a fundamental and prevalent task for the evaluation of large language models (llms) @xcite @xcite . in mcqa, llms are asked to select the most suitable answers from given candidate options based on their comprehension of corresponding questions, as exemplified in figure 1 . to ensure an accurate assessment and a fair comparison among different llms, we always expect these models to respond robustly in mcqa. unfortunately, previous researches have demonstrated certain sensitivities of llms in several aspects. for instance, the arrangement of few-shot demonstrations @xcite , the number of options @xcite and even the order of options @xcite can significantly impact llm's performance in mcqa. relevant studies @xcite suggest that these sensitive behaviors partially reflect the limited symbol binding capacity of llms. namely, llms struggle to associate the content of options with the corresponding symbols, which also raises the question: does the choice of option symbols influence the symbol binding capacity? if so, to what extent can it impact the llms' performance in mcqa? through extensive experiments, we find that the option symbols do matter to llms. specifically, when only the option sym-bols are altered (e.g., a/b/c/@xmath0, the responses generated by llms could exhibit a notable difference (as illustrated in figure 1 ), resulting in an accuracy margin of approximately 10%. in other words, llms demonstrate sensitivity to multiple-choice option symbols, which we define as the option symbol bias. given the outstanding performance of llms in numerous tasks @xcite @xcite , it is pertinent and essential to delve into the causes of aforementioned vulnerabilities and to address them, thereby facilitating the development of more robust and reliable llms. regrettably, despite the analyses of training data @xcite and investigations treating llm as black-box @xcite , the intrinsic mechanisms underlying the llm sensitivity still remain mysterious, due to the complex and non-linear interactions among densely-connected layers @xcite . in this paper, we aim to uncover the inner mechanisms underlying aforementioned option symbol bias, interpret these mechanisms in a way that is comprehensible to humans, and ultimately mitigate the bias. concretely, we investigate the internal components of llms in mcqa task using a causal intervention method known as path patching @xcite , which perturbs the activations of attention heads within the transformer architecture @xcite to observe the causal effects under given metrics. in this work, by measuring the logit difference @xcite after perturbation, we identify a small subset of attention heads that appear to be responsible for this bias. subsequently, the roles of these key components are interpreted in a human-understandable way, suggesting that attention heads with higher causal effects are more likely to excessively focus on the option symbols. we propose that this may be due to the fact that modern llms are mostly pretrained in a next-token prediction paradigm @xcite @xcite . as a result, when performing mcqa, the models tend to search for the most suitable next-tokensobviously, the option symbols-of given questions, ultimately resulting in a special focus on the option symbols. in contrast, the heads with lower causal effects tend to distribute their attention across the content of questions and options. beyond identifying and interpreting the key components, the correlation between causal effects and the attention patterns of these components also motivates us to mitigate the option symbol bias based on the causal effects. specifically, we propose a tuning-free, causal effect driven debiasing (cede) method to alleviate such bias. in cede, the activations of identified key components are steered towards the debiased direction according to the causal effects, with stronger interventions applied to components exhibiting higher causal effects. experimental results demonstrate the effectiveness of the proposed debiasing method. it is noteworthy that this work does not aim to prove that currently used option symbol set (i.e., a/b/c/d) is not the best choice for llms, nor does it seek to find a better option symbol set for mcqa. instead, our intention is to leverage the observed option symbol bias to explore the intrinsic mechanisms within llms during mcqa, which provides insights into the emergence of certain behaviors. the main contributions of this paper can be summarized as follows: ## exploring the existence of option symbol bias in this section, we explore the existence of option symbol bias across various types and parameter sizes of llms, including llama-2-7b/13b/70b @xcite , llama-3-8b @xcite , and mistral-7b @xcite . we evaluate the mcqa performance of these llms on mmlu @xcite , race-middle @xcite and race-high, which are widely used benchmarks for llm evaluation. more details about the dataset and evaluation process are described in appendix a. for option symbols, we select some commonly used formats such as a/b/c/d, 1/2/3/4, i/ii/iii/iv, etc. particularly, we also include ## understanding the mechanisms behind the option symbol bias despite the widespread existence of option symbol bias, it remains unclear what happens within llms that lead to this. in this section, we firstly identify the key components responsible for such bias through causal intervention, then analyze the internal patterns to decode their roles during mcqa. ## conclusion in this paper, we systematically investigate the sensitivity of llms to option symbols, defined as option symbol bias, when performing mcqa. we firstly demonstrate the ubiquity of such bias by conducting extensive experiments on various llms with different option symbols. then, we delve into the internal components within llms through a causal intervention method known as path patching. through path patching, we not only identify a subset of attention heads responsible for the sensitivity, but also interpret their roles through the extracted attention patterns. our exploration reveals that the bias can be attributed to the inner excessive and sparse focus on symbol tokens, which further relates to the next token prediction paradigm in pre-training. finally, we propose a inference-time debiasing method to intervene the activations of identified components based on their causal effects. expriments demonstrate the effectiveness of proposed method. we advocate for increased research efforts directed towards exploring the true capacity of llms, emphasizing the need to better address llm sensitivities during the pre-training stage.