Artifacts or Abduction: How Do LLM s Answer Multiple-Choice Questions Without the Question? (2024)

Abstract: Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. To help explain this behavior, we conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference. Our key findings are threefold. First, we find no evidence that the choices-only accuracy stems from memorization alone. Second, priors over individual choices do not fully explain choices-only accuracy, hinting that LLMs use the group dynamics of choices. Third, LLMs have some ability to infer a relevant question from choices, and surprisingly can sometimes even match the original question. We hope to motivate the use of stronger baselines in MCQA benchmarks, the design of robust MCQA datasets, and further efforts to explain LLM decision-making.

## introduction multiple-choice question answering (mcqa) has been key for evaluating large language models (llms), valued for its ease of scoring and alignment with human testing protocols @xcite . in this task, a model is given a question and list of choices as input, and must select the choice that best answers the question. this process is intended to assess if llms comprehend, use relevant pretraining knowledge, and reason via information from both the question and the choices. since mcqa is meant to evaluate several model abilities, it is often used to rank llms in benchmarks such as the open llm leaderboard @xcite and the holistic evaluation of language models @xcite benchmark. given this role of mcqa, it is essential to verify that mcqa accuracy reflects the abilities we intend to measure. while this concern has been studied with prompt sensitivity @xcite , an unexplored aspect of mcqa-based llm evaluation is dataset artifacts-patterns or biases in text that models may use as shortcuts, rather than completing the task as intended. researchers have found that partial-input baselines @xcite , models trained using just a subset of inputs, can exploit artifacts in parts of the input. such models include hypothesis-only models in natural language inference @xcite and passage-only models in reading comprehension @xcite . if llms rely on artifacts, our datasets may not only fail to assess the skills they are designed to, but we may also overestimate llms and face generalizability issues in deployment @xcite . thus, to gauge if mcqa measures llm abilities as intended, we probe if llama-2 @xcite , falcon @xcite , phi-2 @xcite , and mixtral @xcite ) can use artifacts on three benchmarks: arc @xcite , mmlu @xcite , and hellaswag @xcite . to do so, we test a partial-input choices-only model in mcqa, which omits the question and only sees the choices. concretely, we propose partial-input prompts, a strategy analogous to trainable partial-input models, where only a subset of inputs are used in a prompt. for our tested llms and datasets, we find that in 11/12 cases, choices-only accuracies significantly surpass majority baselines, with a sizeable gain of 0.33 in llama on hellaswag. notably, this is achieved with minimal few-shot examples, ranging from 5 on mmlu to 25 on arc. to give more insights, we design tests to help explain how llms obtain high choices-only accuracy and see if this behavior stems from surface-level shortcuts usually associated with artifacts @xcite , or if more advanced reasoning is employed. we test three hypotheses, shown in figure 1 (right). to quantify the impact of individual priors on choices-only accuracy, we prompt the llm to classify the correctness of each choice in isolation (figure 1 , middle). this prompt only sees one choice at a time and thus the llm can only use its individual priors. in several cases, the individual priors alone cannot explain the high choices-only accuracy, implying that llms reason over all choices rather than just using cues from individual choices. aqi performs similarly to the choices-only prompt, even besting it for 3 llms on hellaswag, and they also exhibit moderate agreement, implying that aqi may be just one of many strategies llms use in choices-only settings. we then study the questions generated by llama on arc. out of the 43 cases when aqi picks the gold answer and the inferred question is answerable by one of the choices, 42% match the meaning of the original question. thus, we argue that a successful choicesonly prompt does not always mean that the llm uses surface-level shortcuts linked with artifacts; in some cases, it may recover the missing question. our intention is not to discredit mcqa evaluations. instead, by highlighting the high choicesonly accuracy of llms and investigating several potential explanations, we aim to give suggestions to enhance transparency and robustness in benchmarking, as well as provide insights into the decision-making of llms in partial-input settings. our contributions can be summarized as follows: ## performing mcqa with no question to test if llms exploit mcqa artifacts, we design partial-input choices-only prompts ( §3.1), and show that these prompts often largely best majority baselines foot_3 ( §3.2). high partial-input accuracies typically imply artifact exploitation via simple shortcuts @xcite . however, in §6 we explore if some of this overperformance can be attributed to the llm's ability to infer the original question. in this case, it would be difficult to claim that the model has bypassed the intended mcqa format. thus, in this section, we establish an initial ceiling for artifact exploitation in the choices-only setting. ## hypothesis 1: memorization our first hypothesis to explain choices-only accuracy is test set leakage, where the llm is trained on the test set @xcite . if this occurred, the llm could recall the answer via memorization @xcite . to test this, we design prompts only answerable via memorization ( §4.1). we assess these prompts and find that memorization cannot explain choices-only accuracy ( §4.2). ## hypothesis 2: choice dynamics with no evidence of memorization in §4, we study choice dynamics in mcqa. we define two properties of answer choices that could account for the llms' high accuracy in choices-only prompts: 1) individual priors: llms may learn strong priors over specific choices from in-context learning or pretraining. for instance, an llm may believe a priori that "albert einstein" is often correct, or subtly that choices containing "not" are often wrong, informing its decision in the choices-only setting. in this section, we tease apart these two factors of choice dynamics. we first design a prompt format that tasks llms with classifying the individual correctness of choices ( §5.1), isolating the effect of individual priors on choices-only accuracy. next, we develop a scoring system to make these binary classification scores directly comparable to the accuracy of the choices-only prompt ( §5.2). finally, we assess the relation of individual priors and metastrategies to choices-only accuracy ( §5.3). ## hypothesis 3: question inference given the potential use of meta-strategies ( §5), we see if llms can use abductive reasoninggiving the best rationale for an observation (peirce, 1974)-in choices-only settings. inspired by work using llms to verbalize inferences @xcite , we test this via abductive question inference (aqi). below, we design ( §6.1), evaluate ( §6.2), and qualitatively analyze ( §6.3) aqi. ## conclusion we find that llms can perform mcqa without access to the question, a result that can be achieved through few-shot prompting with minimal examples. in pursuing explanations for these results, we release an in-depth black-box evaluation suite, based on our proposed partial-input prompts. as part of our analysis, we show that llms still exhibit reasoning abilities in choices-only settings via the meta-strategy of question inference, suggesting that partial-input abilities of llms should not be completely attributed to surface-level shortcuts linked to artifacts. along with software and analysis, we suggest ways to evaluate llms on mcqa as intended, where both the question and choices are needed, including: 1) reporting stronger baselines like our proposed choices-only prompt; and 2) designing robust data creation protocols. in light of our findings, we advocate for more critical discussions around llm evaluation, allowing us to better understand and interpret the model capabilities our benchmarks are truly assessing.