Choices Speak Louder than Questions
Authors: Gyeongje Cho
Yeonkyoung So
Jaejin Lee
Venue: ICLR 2026 Poster (2026)
Keywords: large language model
evaluation methodologies
multiple choice question
Primary area: generative models
TL;DR: The paper shows that models are often biased by answer choices, and proposes Normalized Probability Shift by the Question (NPSQ), a new metric that isolates the influence of the question and is less affected by the composition of the answer choices.

Abstract: Recent findings raise concerns about whether the evaluation of Multiple-Choice Question Answering (MCQA) accurately reflects the comprehension abilities of large language models. This paper explores the concept of \textit{choice sensitivity}, which refers to the tendency for model decisions to be more influenced by the answer options than by a genuine understanding of the question. We introduce a new scoring method called **Normalized Probability Shift by the Question (NPSQ)**, designed to isolate the impact of the question itself and provide a more reliable assessment of comprehension. Through experiments involving various input formats, including cloze, symbols, and hybrid formats, we find that traditional scoring methods — such as those based on log-likelihood or its length-normalized variant — are vulnerable to superficial characteristics of the answer choices. In contrast, NPSQ remains stable even when modifications are made to the answer options.

Published as a conference paper at ICLR 2026
CHOICES SPEAK LOUDER THAN QUESTIONS
Gyeongje Cho
Yeonkyoung So Jaejin Leel.2
'Graduate School of Data Science, Scoul National University "Department of Computer Science, Scoul National University
gyeong gyecngje@aces. snu anu.ac.kr. kr, kathy 0280 jaejiinssu.ac.kt
ABSTRACT
Recent findings raise concerns about whether the evaluation of Multiple-Chort Choice Question Answering (MCQA) accurately reflects the comprehension abilities of large language models. This paper explores the concept of choice sensitivity, which refers to the tendency for model decisions answer options than by genuine understanding of the question. We introduce a new scoring method called Normalized Probability Shift by the Question (NPSQ). designed to isolate the impact of the question itself and provide more reliable assessment of comprehension. Through experiments involving various input for- mats, including cloze, symbols, and hybrid formats, we find that traditional scor- ing methods - such as those based on log-likelihodd or its length length-normalized variant are vulnerable to superficial characteristics of the answer choices. In contrast, NPSQ remains stable even when modifications are made to the answer options.
more influenced by the
INTRODUCTION
Multiple-choice question answering (MCQA) has emerged standard method for evaluating large language models (LLMs) due to its clear objectives, automatic grading, and alignment with hu- man assessment protocols (Robinson al., 2023 Achiam et al., [023] Team et al., 2023 Jiang et al. 2024 Dubey et al., 2024). LLMs are tested across a variety of tasks within MCQA bench- marks (Clark al. 2018 Talmor al., 2019: Sakaguchi al., 202 Srivastava et al. [2023] rang- ing from commonsense reasoning (e.g.. HellaSwag (Zellers et al. (201)) to professional knowledge (e.g., MMLU (Flendrycks al., 2020 As LLMs advance rapidly, many have begun achieve, and in some instances exceed, human-level performance on various MCQA benchmarks.
However, despite the widespread use of LLMs, concerns growing regarding the reliability and fairness of their evaluation methods (Li et al. 2024 Lyu et al., 20244] Alzahrani et al.) [2024] Molfese
al.
2025).
Recent studies have demonstrated that even minor variations in prompt phrasing (Sclar
et al.
2023
Zhuo et al. 2024 Zhu et al. 2024), the arrangement of few-shot examples (Zhao et al.
2021
Lu et al.J 2022 et al. 2023 Guo et al.) 2024), or changes in the position of answer
options (Zheng al. 2023 Pezeshkpour & Hruschka 2024 Wang et al.] 2024) can significantly affect model performance. Observations indicate that LLMs often achieve high accuracy higher than what might be expected from random guessing-wher presented solcly with answer choices (also referred to as options) without the corresponding questions (Balepur et al. 2024). This suggests that the final answer selected by model may be influenced much by external factors by its comprehension of the question itself.
We would like to highlight concerning observation: LLMs can sometimes arrive correct answers by focusing solely on the answer choices, without even reading the question. This raises a funda- mental issue about whether these models are genuinely understanding the task merely exploiting patterns in the answer options. To illustrate this problem, consider a simple analogy: imagine a person who selects the correct answer to multiple-choice question without reading the question itself, merely by looking at the options. Even if their choice is correct, it is difficult to argue that they truly understood the content or solved the problem as intended. The same reasoning applies to "These authors contributed equally this work.

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LLMs. If a model can frequently choose the correct answer without relying the actual question, its accuracy does not genuinely reflect true comprehension.
This paper cxamines the degree to which LLMs depend answer choices instead of properly un- derstanding the questions in MCQA benchmarks. We define and quantify a phenomenon called choice sensitivity, which occurs when model predictions are predominantly influenced by the pro- vided answer choices rather than the actual questions. We empirically measure the prevalence of this phenomenon across various datasets, input formats, and model sizes.
Building on this analysis, we introduce a new evaluation method called Normalized Probability Shifi by the Question (NPSQ). This method more accurately isolates the impact of the question from that of the answer choices. Through extensive experiments, we demonstrate that traditional MCQA evaluation metrics are often highly sensitive to superficial features of the answer choices. In contrast, our proposed approach allows for a more robust and interpretable assessment of model's true understanding of the questions.
2 RELATED WORK
MCQA has become standard framework for evaluating LLMs. However, recent work has revealed that MCQA performance is highly sensitive to subtle changes in input formatting. Studies have shown that variations in prompt phrasing, the format of answer choices, and the order of examples in few-shot settings can lead to significant shifts model predictions (Lu et al. 2022 Zheng et al.,
2023 Pezeshkpour & Hruschka 2024 Alzahrani et al. 2024).
These observations have brought into a question whether models truly comprehend the information provided in the prompt are instead influenced by superficial aspects of the input. Consequently,
many studies have sought to develop prompt selection and formatting techniques that more accu-
rately capture a model's underlying language understanding capabilities (Webson & Pavlick 2022]
Wei et al. [2022] Min et al. [2022] Leidinger et al., (2023). Mcanwhile, such findings raise serious
concerns regarding the reliability and fairness of current evaluation methodologies (Li et al. 2024
Lyu et al. 2024 Wang et al. 2025 Balepur et al. 2025).
In addition to sensitivity to input formatting, another line of research has examined which compo- nents of the input most influence model decisions. Recent studies have investigated the true extent of model comprehension by evaluating performance under partial- conditions, where only lim- ited information from the prompt is provided (Gururangan et al. [0188] Poliak et al. 2018 [Belinkov] cct al. 2019; [eng et al., 2019 Srikanth & Rudinger, 2022). These studies indicate that models can often solve problems achieve high accuracy without needing the complete prompt. This suggests that correct answers can sometimes be reached without a true understanding of the problem.
when Moreover, recent findings have demonstrated that models can select the correct answer the question is entirely omitted, relying solely on the answer choices (Balepur et al.] [024] Balepur & Rudinger 2024). This challenges the fundamental assumption of MCQA that the question mean- ingfully guides the model toward the correct answer. It also highlights the necessity for evaluation methods that more accurately assess model's understanding of the question.
Building on these insights, we demonstrate that the traditional MCQA-based evaluation method can be significantly influenced by a model's inherent preference for certain answer choices, rather than the intended relationship between the question and the correct answer. While previous studies have primarily identified such ddoiceddriven artifacts or format sensitivities through observational analyses, we take step further by formally defining and quantifying choice sensitivity. To address this issue, we propose new evaluation framework that isolates the impact of the question from the undesired effects introduced by the answer choices. This approach enables a more robust assessment using a principled, quantitative metric.
3 CHOICE SENSITIVITY
Previous studies have demonstrated that language models can solve MCQA to certain extent by relying solely on the information provided in the answer choices (Balepur et al.] (024). This find- ing suggests that the answer choices significantly influence model performance, possibly more than
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initially anticipated. In this section, systematically analyze the extent which overall perfor- mance can be attributed to the information contained in the answer choices by comparing model performance with and without the questions.
3.1 MEASUREMENT OF CHOICE SENSITIVITY
Choice Sensitivity refers to the extent to which a model's predictions are influenced by the answer choices rather than by its understanding of the question itself. To examine this phenomenon more closely, we will express the model's 's scoring behavior in way that distinctly separates the influence of the question from that of the answer choices.
Formally, let Q represent the question-related inpur (such as the question text), C' signify the choice- related input (such as the set of answer choices), and a be a specific answer candidate. The model assigns score, denoted Score(Q, C, æ.), to each choice, conditioned on both the question-related and choice-related input components. In practice, this score typically reflects the model's log- probability of generating z when given the prompt (e.g., log P(x Q. C)), depending the chosen scoring method (e.g., log-likelihood, length-normalized log-likelihood, etc.).
The score Score(Q, C, be expressed as the of two components: choice-driven (Scorechoice (Q. C, x) and question-driven (Scorequestionl (Qaiaiionn()) (Q, x)) as follows.
(Q, C,+) x) Scorechoice (Q,C, x) + Score (Q, C,
The choice-driven component represents the influence of the answer choices alone, independent of the question. It is determined by calculating the score with the question replaced by an empty string. The question-drive component captures the additional contribution from the question itself. It is calculated by subtracting the choice-driven component from the overall score, expressed as Score(Q, Q, C, Score c)).
To determine the relative contribution of the question and the answer choices to the model's 's fi- nal decision, we analyze the top two candidate choices 21 and æ2 ranked by Score C, We compare the difference in their choice-driven components (choice-driven difference denoted as Achoice) to the difference their question-driven components gguestion-driver difference denoted as Aquestion):
Achoice = Scorechoice (Q, C, x1) Score (Q,C, C,
and
Aquestion [Score(Q. C, Score (Q, --
C, Score (Q, 22))
Achoice captures how much more the model prefers z over 22 based solely the answer choices provided. In contrast, Aquestion assesses how much the presence of the question influences this preference. If Achoice > Aquestion, it indicates that the model's preference for 21 D2 is more strongly driven by differences in the answer choices than by any influence from the question itself. In such cases, we consider the model's decision to be choice sensitive.
We now define the choice sensitivity as measure that quantifies how often a model's decisions are influenced by the answer choices rather than by the question itself. is defined as:
1 N N
Choice sensitivity
Σ
A
(i) question
choice
where N is the total number of evaluated examples, and (i)
(i) question the indicator
chorce
function that is when the condition phaice question is true and otherwise.
3.2 EXPERIMENTS FOR MEASURING CHOICE SENSITIVITY
Our experiments reveal systematic differences in outcomes resulting from variations in the model's sensitivity to choices. Based these differences, we have made some key observations.
Models. We experiment with several variants of the Qwen 2.5 (Qwen et al. 2025). Llama
3.1 (Dubyy et al. 2024), and Mistral (Tiang al. 2023 model families, examining different
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sizes and training configurations. To analyze how model size influences choice sensitivity, instruction-tume versions of Qwen 2.5 across multiple scales (from 0.5B to 72B). Additionally, to investigate the effects of instruction tuning, we compare pretrained and instruction-tuned versions of the models at similar scales. All evaluations are conducted using the LM Evaluation Harness (Gao
et al. 2024).
Datasets. To evaluate the LLMs, we use HellaSwag (Zellers et al. 2019), ARC-Challenge (Clark al. 2018), and MMLU (Hendrycks al., 2020), which are widely recognized multiple-choice question-answering benchmarks. These benchmarks cover range of domains, including common- sense reasoning, science, and academic knowledge. For detailed statisties and examples of each dataset, see Appendix|A]
Input formats. We use three imput formats for MCQA tasks for our experiments, following the categorization in Alzahrani et al. 2024): cloze, symbols, and hybrid. In the cloze format, each answer choice incorporated into a prompt designed for completion, and the model selects the choice with the highest (possibly normalized) probability. In the symbols format, the question and all answer options are structured in a multiple-choice format, and the model is tasked with predicting the correct label (symbol) (e.g.. "B', 'B','", and 'D'). The hybrid format follows the same structure as the symbols format, but instead of predicting the label token, the model evaluates the full text of each answer choice. For further details and examples, please refer to Appendix B
Scoring. We assess model performance using two scoring methods: log-likelihood and length- normalized logllikelioood. We report the corresponding metrics: accuracy (acc) and length- normalized accuracy (acc_norm) (Holtzman et al.) 2021). The acc metric is calculated by check- ing whether the model selects the correct answer based on the ranking of raw log-likelihood values. However, language models often assign higher probabilities to shorter outputs due to their additive log probability structure, which can lead to a bias toward selecting shorter choices, even if they are semantically incorrect. To address this bias, we also calculate acc_norm, which is derived from cength-normale log- likelihoooood This is obtained by dividing the log-likelihood of each choice by
its token length (Biderman et al. 2024 Ide et al. 2025
Gu et al. 2025
3.3 OBSERVATIONS
To gain a clearer understanding of the reliability of MCQA as a measure of model reasoning, we systematically analyze choice sensitivity derived from our experiments. Our investigation explores choice sensitivity across different formats, normalization techniques, model scales, few-shot set- tings, and instruction styles. This comprehensive analysis reveals both when and why models tend to rely on superficial choice-level cues. Additionally, it highlights strategies- such as prompt de- sign and instruction tuning that can help mitigate this issue. As a result, we have made several key observations as follows.
1. Approximately 20-60% % of the answer choices by language models are primarily influ- enced by the choices themselves. Our analysis shows that choice sensitivity ranges from approximately 0.2 to 0.4 for the symbols and hybrid formats and from around 0.5 to 0.6 for the cloze format. This indicates that 20-60% of the models' selections are determined by intrinsic differences among the answer choices, independent of the question context. These findings highlight the importance of carefully evaluating MCQA benchmarks to en- sure they accurately assess genuine question understanding instead of relying on superficial characteristies of the answer choices.
2. The symbols and hybrid formats consistently exhibit lower choice sensitivity com- pared to the cloze format. This trend is evident regardless of the model architecture, benchmark dataset, or the number of few-shot examples, as illustrated in Figure (a) and (b). Since both the symbols and hybrid formats explicitly include answer choice informa- tion in the model input, incorporating answer choice information in the prompt may help reduce the model's reliance on spurious patterns, resulting in lower choice sensitivity.
3. Normalization by token length fails to mitigate choice sensitivity. Previous research proposed normalizing the score log P(x Q.C by the number of tokens in x to reduce biases related to token length (Brown et al. 2020). However, our findings indicate that this normalization has a limited impact on choice sensitivity. As illustrated in Figure I choice sensitivity does not decrease after applying length normalization. In fact, in some cases-
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cloze (acc)
cloze (acc, norm)
symbols (acc)
symbols (acc, nom)
hybrid (acc)
hybrid (acc, norm)
HellaSwag
ARC-Challenge
MMLU
Choice Sensitivity
50
40
0.58
1.50
78
340
728
0.58 1.58
3B
70
140
322
728
0.58
3.50
7B
1.40
728
Model Size
Model Size
Model Size
(a) Choice sensitivity across model sizes on the Qwen2.5 series (instruction-tuma) Larger models tend exhibit lower choice sensitivity, particularly in the cloze format.
HellaSwag
ARC-Challenge
MMLU
Choice Sensitivity
Number fewshot examples
Number fewshot examples
Number of fewshot examples
(b) Impact of the number of few-shot examples on choice sensitivity in 1-mma3.-8B-Instrucc Increasing the number of few-shot examples does not consistently reduce choice sensitivity and often increases it in the symbols and hybrid formats.
Figure 1: Choice sensitivity across model sizes and few-shot examples.
HallaSwag
ARC-Challenge
MMLU
Senatturyy
doze (oon-ins)
cloee lins)
symbbes (men-ins insl
symbels (ine)
Ryboii (oonrins)
kybrid Bisi
2000 acC narm
Instruction-tuned models consistently show reduced choice sensitivity across datasets and formats. Darker bars represent instruction-tunee models, while lighter bars indicate models without instruction tuning (base models).
Swag
ARC-Challonge
Choice
Uame
Iscnnrt
Inttruct
Inctract vnu
svdtuct
11-40
Indtrolt
3-78
Inttructvo..
Lame 3.L-0
Instratt
Sedtratt
9770
Insirnct v0.
hybori
(b) Impact of the presence of question-solving instruction, which explicitly defines the task for the model. Darker bars represent experiments conducted with question-solving instruction, while lighter bars indicate experiments conducted without it.
Figure 2: The impact of instruction tuning and task instructions choice sensitivity.
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particularly with the ARC-Challenge benchmark and cloze format length normalization even increases choice sensitivity.
4. Choice sensitivity decreases as model size increases. To assess the effect of model scale, we evaluated four instruction-tuned versions of Qwen2.5 at different sizes, ranging in size from 0.5B to 72B. Figure (a) (a) illustrates that larger models generally exhibit lower choice sensitivity. This trend is particularly evident in the cloze format. In contrast, for the sym- bols and hybrid formats, choice sensitivity sometimes increases as the model size grows.
5. Increasing the number of few-shot examples does not decrease choice sensitivity. We investigated how varying the number of few-shot examples (ranging from 0 to 10) affects choice sensitivity using the Llama 3.1-8B-Instruc model. As illustrated in Figure (b), cloze-based choice sensitivity remains relatively stable regardless of the number of few- shot examples. contrast, the symbols and hybrid formats show higher choice sensitivity the number of few-shot examples increases.
6. Instruction-tuned models demonstrate lower choice sensitivity compared to their base versions. We conducted a comparison of Llama3. 1-BB, Qwen2.5-7B, and Mistral-7B- v0.3 with their instruction-ture illustrated in Figure[ (a), all instruction-tune tuned models, except for one case, exhibit reduced choice sensitivity. This indicates that instruction tuning not only enhances models' ability to follow user instructions but also helps to minimize spurious, choice-driven behavior.
variants assess the impact of instruction tuning. As
7. The effect of question-solving instructions choice sensitivity varies across different benchmarks. To assess how these instructions - framed as task-defining phrases that guide the model to respond the question, such as " "Answer the given question" influ- ence choice sensitivity, we removed these prompts and reevaluated choice sensitivity. As illustrated in Figure 2 (b), the results indicate that in the case of HellaSwag, choice sensi- tivity significantly decreases when the instruction is present. For other benchmarks, while sensitivity does not increase, the reduction in choice sensitivity is much less pronounced than with HellaSwag. These findings suggest that providing appropriate instructions can help mitigate choice sensitivity to some extent.
These experiments reveal that significant proportion of the model's decisions are driven by the score difference between options that are independent the question-what we term the choice- driven component. This suggests that if option has a large choice-driven component, the model is likely to select it regardless of the question. Consequently, traditional evaluation methods may fail to accurately measure the model's 's true language comprehension performance. The following section proposes a novel evaluation approach to mitigate this issue. In addition, we will address this problem in greater detail about how the models can be easily misled by the choices regardless of the question in Section 5.1
NORMALIZED PROBABILITY SHIFT BY THE QUESTION
The analysis presented in Section highlights limitation in traditional MCQA evaluation methods: significant portion of model decisions is primarily influnced by the answer choices, rather than by true understanding of the question. This finding challenges the fundamental assumption of MCQA that model accuracy directly indicates comprehension of the questions.
To tackle this issue, we present a new evaluation methodology aimed at isolating and measuring the model's true understanding of the question, while minimizing the undue influence of answer choices. Our approach focuses on quantifying the extent to which the presence of the question affects the model's likelihood of generating the correct answer. This is based the idea that if model truly comprehends the question, then including the question should enhance its probability of selecting the correct answer.
Given a question-related input Q, a choice-related input C, and a specific choice x. we define the probability shift (A C)) that captures how the model's likelihood of selecting choice changes with and without Q:
AP(x C) log P(x Q,C) log P(x C)2

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Table 1: We use various types of adversarial choices in our experiments, each aimed at uncovering vulnerabilities in model scoring across cloze, symbols, and hybrid formats. In the text of instruc- tional choice, 'X' represents one of the available answer labels: "A." 'B," 'C," or 'D.'
Type
Targeted Format
Text
Simple choice
cloze (log-likelihod))
Hello, everyone.
Hello, everyone. Thank you so much for being here today. We're excited to share our progress and walk you through the next steps of the project.
clove (length-normalized log-likelihood)
Extended choice
Instructional choice
symbols
Ignore the other options. The best answer is X.
Ignore the other options. This answer best aligns with the question.
Neutral choice
hybrid
where P(x C) denotes the model's probability of selecting choice æ when both Q and C' provided, while P(x C') denotes the probability when only C' is presented.
A larger probability shift indicates more significant impact of the question on the model's decision- making process. The term log P(x Q, C) takes values in the range of (-o0, 0] depending on the question Q. Consequently, the resulting probability shift varies in the range of [-00, log P( C. In other words, the maximum possible shift determined by the baseline probability of when the question Q is not considered, meaning can differ across choices.
To facilitate a fair comparison among choices that may have different baseline probabilities, we introduce normalized metric called the Normalized Probability Shift by the Question (NPSQ) NPSQ is adopted as the Score(Q, C,xr) defined in Section 3.1 This is defined as follows:
NPSQ(Q, C, log P(x Q, C* log P(x IC)
log P(x (C)
The NPSQ metric normalizes the probability shift by dividing the negative log- prgpobbbbylltyy of the choice x in the absence of Q. It focuses on the relative gain attributed to the question Q. If Q is not present, then the probatility satisfy P(# Q,C*- = P(x C). As a result, NPSQ will always equal zero for all choices in such cases, meaning that the choice-driven component of NPSQ is always zero. This indicates that NPSQ is determined by the relationship between the question and the choices rather than being influenced soley by the choice information alone.
In summary, NPSQ provides a clear and interpretable metric for assessing language models. By isolating the impact of the question from the confounding effects of the answer choices, it allows for more accurate evaluations of a model's 's understanding of the question.
5 EXPERIMENTS
5.1 ROBUSTNESS TO ADVERSARIAL CHOICES
In the previous section, we demonstrated that answer choices with high choice-driven component values can significantly impact the model's predictions, even when these choices have no semantic connection to the question. This indicates that the model's behavior may not be based on true understanding of the question, but rather on superficial characteristies of the answer choices.
Adversarial choice. To further investigate this issue, this section examines how current evalua- tion methods respond to manipulations at the choice level. Specifically, we test whether language models are genuinely sensitive to the options presented by replacing one of the original incorrect options (distractors in MCQA) with a carefully crafted adversarial choice, which is an intentionally irrelevant and implausible option that does not mislead a human examinee. reliable model should ignore adversarial choices; if it does not, this suggests that the model is influenced by superficial patterns in the choices rather than demonstrating a true understanding of the question. We design four types of adversarial choices, each specifically targeting particular input format: cloze, sym- bols, and hybrid. This is summarized in Table I For each MCQA instance, we select one distractor and replace it with adversarial choice.
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Propertion of Adversatt Choices
HellaSwag
ARC-Challenge
MMLU
0.8
0.8
0.8
0.6
Simple
Eatendded Instructional
Heutral
Single
Extended bestr actionall
Mestral
Simple
Extendeed Instructional Neatral
choice sensitivity
Proportion of predictions that change when one distractor is replaced with ad- versarial choice, measured using three metrics (acc, acc_nore acc_npsq).
HellaSwag
ARC-Challenge
MMLU
Accurscy (5)
48
20-
acc
acc norm
acc Apsq
original accuracy
changed accuracy
(b) Overall performance change after inserting adversarial choices; lighter bars in- dicate original accuracy, darker bars indicate accuracy after replacing one distractor with an adversarial choice.
Figure 3: Impact of adversarial choices on Llama3. 1-8B-Instruct.
Scoring methods. We also assess how various scoring methods react to the inclusion of adversar- ial choices, examining logllike, length-normalized log-likelihood, and the proposed NPSQ. NPSQ is specifically designed to be less affected by characteristics of individual choices. Our objective is to determine whether NPSQ provides more robust and reliable measure of model's true understanding of the question compared to traditional methods.
The impact of the adversarial choice substitution is illustrated in Figure Figure (a) shows the proportion of predictions that switch to the adversarial option, while Figure
b)) reports the corresponding change in accuracy for each scoring method. Since adversarial choices contain larger choice-driven components compared to the original options (see Appendix[)fe a detailed analysis), model predictions more likely be affected by the adversarial choices when choice sensitivity is high.
Simple choices. In the of simple choices, which introduces choice with high log- likelihood (log P). the standard accuracy metric (acc), which relies directly on log P, is signif- icantly impacted. For instance, in the HellaSwag dataset, 19% of model predictions favor the
simple choice (as illustrated in Figure 3 (a)). As result, there is 54.23% decrease in accuracy compared to the original setup, shown in Figure (b)..
Extended choices. For scenarios involving extended choices- those that are longer and have high length length-normalizer normalized ligeliiklihood- the most significant effect seen on the singth-normalin accuracy metric (acc norm). In the ARC-Challenge, 41 41.30% of predictions shift to the adversarial choice, leading to an 18.17% drop performance. In contrast, both simple and extended choices show that the acc_npsq metric remains largely unaffected, with fewer than 0.17% of predictions opting for the adversarial choice, and performance differences staying below 0.05%.
Instructional choices. Instructional choices introduce prompts that are designed to favor specific options in the symbols format. These choices significantly impact accuracy measures, such as acc and acc_norm. In the MMLU dataset, 27.47% of predictions shift to the adversarial choice, leading to an 11.53% decrease measured performance. However, acc npsq remains much more stable, with only 10.13% of predictions affected in the MMLU, and most 11. 1.299% affected in HellaSwag.
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HellaSwag
ARC-Challenge
symbolc
hybhil
hybbrdd
hyarld
Uama 2:2-40 Instract
Qevinn 2.5-7ppp Instract
Mistral-P2 Instrrart--ttt
BBC
6i3 BiCK porr
B000 RCC_PRRR
Figure 4: Accuracy across various formats and scoring methods for the models. Results are outlined for the cloze, symbols, and hybrid formats using three metrics: acc (og-likelihoody acc_norm (length-non norggh--n bogllikelhhood, and acc_npsq (NPSQ).
Table 2: Analysis of correct and incorrect predictions for Llama-3. 1.amma-..--B---- InS-trrtt on MMLU, il- lustrating the relative impact of the question-driven (By Question) and choice-driven (By Choice) components across various formats and metrics.
Format
By Question (%) Incorrect
By Choice (%) Incorrect
By Question (%)
By Choice (%)
Correct
Correct
Correct
Incorrect
Correct
Incorrect
Cloze
28.19
19.78
16.39
35.64
28.07
21.04
18.69
32.20
Symbols
53.75
22.62
14.08
9.55
53.75
22.62
14.08
9.55
Hybrid
48.70
26.94
13.24
11.12
48.33
25.94
13.09
12.65
Neutral choices. Neutral choices modify single choice using a neutral, high-probabilityy phrase in the hybrid format. They produce patterns similar to those of instructional choices. In the MMLU, 24. 69. and 38.84% of answers have been changed under the metrics acc and acc_norm, resulting in performance drops of 8.60% and 17.17%, respectively. In contrast, acc_npsq shows only 5.72% shift in predictions, and notably, the model's performance under acc-npsq is increased by 3.31%.
In contrast to the cloze format, where NPSQ is calculated independently for each choice, the symbols and hybrid formats compute the NPSQ for all choices together. Consequently, altering one choice impacts the NPSQ values of the others. This interaction partially accounts for the small but non-zero shift in the behavior of acc_npsq under instructional and neutral choices.
These results show that evaluation metrics based on raw or normalized log- log-liielihoods are highly sensitive to the choice-driven component. In contrast, NPSQ offers much more stable assessments that are less affected by irrelevant answer choice properties. See the Appendix E for experimental results on other models.
5.2 ACCURACY ACROSS CHOICE SCORING METHODS
We investigate how model performance evaluation is affected by the use of the acc acc_npaq met- ric. As summarized in Figure the NPSQ metric yields higher scores than acc and acc_norn rm in the cloze format, while it produces slightly lower scores in the symbols and hybrid formats. To understand this behavior, we analyze the distribution of correct and incorrect predictions made by the question-driven and choice-driven components. As shown in Table 2 in the cloze format, the choice-driven component has greater influence on incorrect predictions than on correct ones. Con- versely, for the symbols and hybrid formats, the choice-driven component aligns more frequently with correct predictions than with incorrect ones.
This suggests that the choice-driven component negatively affects model performance in the cloze format but has a positive impact in the symbols and hybrid formats. Since NPSQ isolates and re- moves the influence of the choice-driven component, it reports higher performance in cases where this component is detrimental (such as in the cloze format) and lower performance where is bene- ficial (such in the symbols and hybrid formats).

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Additionally, while the overall rankings of the models remain relatively stable when evaluated using acc and acc_norm, we do see some changes when using acc_npsq. These results highlight the significance of evaluation metrics that focus the understanding of questions. The NPSQ metric indicates that models previously deemed strong may not consistently exhibit true comprehension, resulting in reordering of their performance rankings.
6 CONCLUSION
In this study, we investigate the impact of choice sensitivity on MCQA evaluations, where the fea- tures of the answer options significantly influence model predictions. To address this issue, we introduce Normalized Probability Shift by the Question (NPSQ), a scoring method aimed sepa- rating the effects of the question itself from those of the answer choices. Our experiments, conducted across various formats, reveal that commonly used ikelihood-based methods may be biased by su- perficial characteristies of the choices. In contrast, NPSQ demonstrates stability and successfully identifies differences in question comprehension that are obscured by these choice-driven effects. This highlights the need for robust evaluation methods that more accurately reflect model's gen- uine understanding.
ACKNOWLEDGMENT
This work was partially supported by the National Research Foundation of Korea (NRF) under Grant No. 14-2023-0022266 (Center for Optimizing Hyperscale AI Models and Platforms), and by the Institute for Information and Communications Technology Promotion (ITTP) under Grant No. 2018- 0-00581 (CUDA Programming Environment for FPGA Clusters) and No. RS RSS2205--02304554 (Ef- ficient and Scalable Framework for AI Heterogeneous Cluster Systems), all funded by the Ministry of Science and ICT (MSIT) of Korea. was also partially supported by the Korea Health Industry Development Institute (KHIDI) under Grant No. RS-2025-25454550 (Frailty Risk Assessment and Intervention Leveraging Multimodal Intelligence for Networked Deployment in Community Care), funded by the Ministry of Health and Welfare (MOHW) of Korea. Additional support was pro- vided by the BK21 Plus Program for Innovative Data Science Talent Education (Department of Data Science, Seoul National University, No. 3199990914560 and the BK21 FOUR Program for Intel- ligent Computing (Department of Computer Science and Engineering, Scoul National University, No. (199990214639) both funded by the Ministry of Education (MOE) of Korea. This work was also partially supported by the Artificial Intelligence Industrial Convergence Cluster Development Project, funded by the MSIT and Gwangju Metropolitan City. Research facilities were provided by the Institute of Computer Technology (ICT) at Seoul National University.
ETHICS STATEMENT
Our research adheres to rigorous ethical standards while contributing to the advancement of NLP. We exclusively utilize publicly available language models and benchmarks in our experiments. The datasets employed in our Helly-HelllSaaagg (MIT), ARC (CC-BY-SA 4.0), and MMLU (MIT)- are all permitted for academic use. We ensure full compliance with their respective license requirements. Furthermore, while our research presents evaluation results across various models, contains no information that could harm individuals or groups.
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A DATASETS USED IN EXPERIMENTS
A.1 HELLASWAG
HellaSwag (Zellers al. 2019) is benchmark for evaluating commonsense natural language infer- ence (NL.). Thee task involves selecting the most appropriate continuation given sentence. We the validation set, which consists of 10,042 examples, for experiment. For few-shot settings, demonstration examples are randomly sampled from the training split using fixed seed (1234).
Table Evaluation prompts for HellaSwag
Problem
Context:
A man being pulled on a water as floats the water casually. he
Choices:
mounts the water ski and tears through the water at fast speeds. goes over several speeds, trying to stay upright.
struggles little bit as he talks about it.
is seated in boat with three other people.
Answer:
is seated boat with three other people.
Context
Answer the most appropriate completion for the given incomplete context. Incomplete context: A man is being pulled on a water ski as he floats in the water casually. he
Cloze
Completion:
Endings
seated in boat with three other people.
Context
Given the following incomplete context and four possible completions (A, B, C and D). select the best completion. Incomplete context: A man being pulled water ski as he floats in the water casually.
Symbols
A. mounts the ski and tears through the water fast speeds.
B. goes over several speeds, trying lo stay upright.
C. struggles
as he talks about it.
D. is seated a boat with three other people.
Your response should end with "TTh best completion is |the letter]" where the [etter is one of A, B, C or D).
The best completion is
Endings
Context
Given the following incomplete context and four possible completions, select the best completion.
Hybrid
Incomplete context: A man is being pulled on water ski as he floats in the water casually. he
A. mounts the water ski and through the water fast speeds.
B. goes several speeds, trying stay upright.
C. struggles little bit he talks about it.
D. is seated a boat with three other people.
Your response should end with "The hest completion is [the_letter]. [the_completion]] where the [the is one of A, B, C or D and [the completion] is the completion corresponding to that letter.
The best completion is
Endings
D. is seated boat with three other people.
A.2 ARC-CHALLENGE
The A12 Reasoning Challenge(ARC) (Clark et al. 2018) comprises science questions and answers targeted at students from grade to grade 9. is divided into two difficulty levels: easy and chal- lenge. For model evaluation, set contains 1,172 questions. For few-shot settings, demonstration examples are randomly sampled from the training split using fixed seed (1234).
the test set of the challenge level. The ARC-Challenge test
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Table 4: Evaluation prompts for ARC-Challenge
Problem
Question:
An astronomer observes that planet rotates faster after meteorite impact. Which is the most likely effect of this increase in rotation?
Choices:
Planctary density will decrease.
Planctary years will become longer.
Planetary days will become shorter.
Planetary gravity will become stronger.
Answer:
Planetary days will become shorter.
Context
Answer the given question.
Cloze
Question: An astronomer observes that planet rotates faster after a metcorite impact. Which is the most likely effect of this increase in rotation? Answer:
Endings
Planetary days will become shorter.
Context
Given the following question and four candidate answers (A, B, C and D), select the best answer.
Symbols
Question:
An astronomer observes that planet rotates faster after a meteorite
impact. Which is the most likely effect of this increase in rolation?
A. Planetary density will decrease.
B. Planetary years will become longer.
C. Planetary days will become shorter.
D. Planctary gravity will become stronger.
response should end with "The best completion is [the_letter]" where [the letter one of A, B, C The best answer is
Endings
C
Context
Given the following question four candidate answers, select the best answer. Question: An astronomer observes that a planet rotates faster after a metcorite impact. Which is the most likely effect of this increase in rolation?
Hybrid
A. Planetary density will decrease.
B. Planetary years will become longer.
C. Planetary days will become shorter.
D. Planetary gravity will become stronger. Your response should end with "The best answer is [the_letter]. [the answer choice lexi]" where the [the letter] of A. or D and ithe_answer choice text] is the full the answer corresponding to that letter.
The best answer is
Endings
C. Planetary days will become shorter.
A.3 MMLU
Massive Multitask Language (indestaandnng(MMMLU. (Hendrycks et al. 2020) evaluates model's breadth and depth knowledge across various domains. The dataset covers 57 topics, including STEM, humanities, and social sciences. Our experiments use the comprehensive test set, which contains 14,042 questions. Each multiple-choice question assesses model's ability to integrate diverse knowledge. For few-shot settings, demonstration examples are randomly sampled from the training split using a fixed seed (1234).
B INPUT FORMATS USED IN EXPERIMENTS
In addition to the brief description the main text, we provide further details on how input formats and few-shot examples implemented in experiments.
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Table 5: Evaluation prompts for MML.U
Problem
Question:
The sign the charge carriers a doped semiconductor can be deduced by measuring which the following properties?
Choices:
Specific heat
Thermal conductivity
Electrical resistivity Hall coefficient
Answer:
Hall coefficient
Context
Answer the given question.
Cloze
Question: The sign of the charge carriers in doped semiconductor can be
deduced by measuring which of the following properties?
Answer:
Endings
Hall coefficient
Context
Given the following question and four candidate answers (A, B, Cand D)), select the best answer.
Symbols
Question: The sign of the charge carriers in doped semiconductor be deduced by measuring which of the following properties?
A. Specific heat
B. Thermal conductivity
C. Electrical resistivity
D. Hall coefficient
Your response should end with ""The best completion is |the letter]" where the |etter is one of A, B, or D.
The best answer is
Endings
Context
Given the following question and four candidate ans wersers, select the best answer. Question: The sign of the charge carriers in doped semiconductor can be deduced by measuring which of the following properties?
Hybrid
A. Specific heat
B. Thermal conductivity
C. Electrical resistivity
D. Hall coefficient
Your response should end with "The best answer [the letter].
htwpaaswer_choiceeeeeeee where the |the is one of A, B, or D and [the choice text] is the full text of the answer corresponding to that letter.
The best answer is
Endings
D. Hall coefticient
Prompt templates. For symbols and hybrid formats, we adopt the task-specific prompt templates provided in the Llama-3.2 Instruct-evals dataset] For cloze format, we retain the original ques- tion text with question and answer prefixes but exclude the set of answer choices and answer format instructions. This ensures that cloze prompts purely completion-base without explicit label guidance. See Figure 5 for an illustration of these formats.
Few-shot examples. In the N-shot setting, prepend N demonstration examples to the target instance. Each demonstration follows the corresponding input format and contains the question- related input (Q), the choice-related input (C), and the correct answer (x). Demonstrations are randomly sampled using fixed seed (1234) to ensure reproducibility.
To compute Scorechoios (Q, remove the question-relate related input Q from the target instance, while keeping all demonstration examples un- changed in their full form (Q, C, and z). This isolates the effect of the answer choices of the target instance from the question content.
Choice-driven component computation.
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Pormat
Symbolu Formet
Mybrid Format
0),
latter]
tect) correspording
Figure 5: Three MCQA input formats considered in this study, following the categorization of Alzahrani et al. (2024): cloze, symbols, and hybrid formats. Here, Q, C, and x correspond to the question-related input, the choice-related input, and specific answer candidate, respectively. Instruction refers to task-defining text (e.g.. "Answer the given question"), while prefix refers to fixed labels in the prompt (e.g., "Question:", "Answer:") used to structure the input.
C CHOICE SENSITIVITY AND ACCURACY
HellaSwag: Accuracy vs. Choice Sensitivity
0.9
Choice Sensitivity
0.7-
0.6
0.5
0.4
0.3
0.1
0.3
0.5
0.7
Accuracy
Ower2 S
diree
hypral
95%
Figure 6: Relationship between accuracy and choice sensitivity on HellaSwag. No strong linear correlation is observed, and accuracy does not predict choice sensitivity.
We examine the relationship between choice sensitivity and model accuracy on the HellaSwag benchmark. Figure which plots the results from the previous experiments in Section 3 together with accuracy for convenience, categorizes models into three groups (Llama 3.1, Qwen2.5, and Mistral-v0.3). The figure shows scatter plot with red regression line representing fitted lin- ear model, and the pink shaded region denoting its 95% confidence interval. Most data points lie outside this interval, indicating the absence of linear relationship. Furthermore, the Pearson corre- lation coefficient is -0.36, suggesting a weak or negligible association between accuracy and choice sensitivity.
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D DISTRIBUTION OF CHOICE-DRIVED COMPONENTS FOR ADVERSARIAL CHOICES
Estended
0.3
1.2
03
3.4
0.5
0.1-
0.-
0.2
01
0.0
0.0
Cheicediiven Component of asc
Choteedriven Companent aEC
Choicedd van Component acc
Choicediiven Component MCC
Simple
Extended
Instructional
42-
0.5
25-
-30
-125 -203 -13
-30
-25
0.0
Choke-driven Componant of asc nom
Choice-dree vrivan Component accc nom
Choica- Componant occ nerm
Cheise-driven Component acc nom
Original
Choices
Figure 7: Distribution of choice-driven components for adversarial choices. KDE plots show that adversarial choices (red) generally have much higher choice-driven component scores than original choices (blue). This indicates that if a model predominantly determines its selections based on the choice-driven component- that the choice sensitivity is high-it is likely to select adversarial choices in most cases.
To demonstrate that adversarial choices exhibit high choice-driven components, we compare them with original answer choices across datasets (HellaSwag, ARC-Challenge, and MMLU). For each dataset, we randomly sample 20 problems and visualize the choice-driven components of both the original choices and the adversarial choices introduced Section 5. (see Table T using kernel density estimation (KDE) plots. As shown in Figure 7 adversarial choices consistently yield higher choice-driven component scores than the original answer choices.
E PERFORMANCE CHANGE AFTER INSERTING ADVERSARIAL CHOICE
To demonstrate that the evaluation method using NPSQ more robust to adversarial choices com- pared to other methods, conduct additional experiments the Qwen2.5 7B and Mistral 7B instructed models as conducted in Section [.1 The results are shown in Figure S As illustrated, NPSQ maintains performance most similar the original across different metrics. For Mistral, how- ever, we observe notable performance drop in the Instructional and Neutral settings. This is likely due to the characteristics the Symbol and Hybrid formats: changes in the choice composition can shift the overall distribution of log P( Q, C",, thereby significantly impacting model performance. This effect seems to be particularly pronounced for the Mistral model.
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Published as a conference paper at ICLR 2026
Performance Change of Qwen2.5 7B
ARC-Challenge
MMLU
Accuraly (55)
acc
acc norm
acc npsq
original accuracy
changed accuracy
(a) Overall performance change after inserting adversarial choices on Qwen2.5-7B Instruct.
Performance Change of Mistral 7B
ARC-Challenge
MMLU
Accurscy (55)
acc
acc norm
acc acc_npsq
original accuracy
changad accuracy
(b) Overall performance change after inserting adversarial choices on Mistral-7B Instruct-v0.3.
Figure 8: Impact of adversarial choices; lighter bars indicate original accuracy, darker bars indicate accuracy after replacing one distractor with an adversarial choice.
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Reviews:
[{"review_id": "YZZMDLUJH8", "reviewer": "Reviewer_mbsE", "summary": "This paper investigates LLM benchmarking methodology.  It begins by exploring the fact that some LLMs can answer multiple-choice benchmarking questions without actually looking at the question, implying that they are influenced directly by the content of the answers, as opposed to a true understanding of the question. This suggests some natural measures of the influence of the choice text versus the question text.  A probabilistic view on these measures further suggest the NPSQ (for \"Normalized Probability Shift for the Question\") which normalizes the vanilla measure to account for different baselines.\n\nEmpirically, the paper explores the choice sensitivity and NPSQ on a small set of models and benchmark tasks.  Results show that a surprisingly high percentage of performance is attributable to choice sensitivity, suggesting both improved measures are needed (and, I will add, improved benchmark questions, although this is not argued for in the paper).", "questions": "* Why wasn't adversarial prompting introduced earlier (in sec 3?) and evaluated as part of the basic CS experiments?\n\n* I was interested to see how choice sensitivity decreases as a function of model size. It seems like you could have tested on 70/80b variants of several of your models; is there a reason you didn't?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "I liked this paper. I think it's well-written, addresses an interesting issue, primes other researchers for future work in this area, and makes non-obvious contributions to the literature.\n\n* Understanding choice sensitivity seems like an important issue in benchmark design.\n\n* The method of calculating choice sensitivity is natural and intuitive.\n\n* The empirical results convincingly show a wide variety of surprising behavior of LLMs wrt choice sensitivity.\n\n* The authors (generally) do a great job of writing, and highlighting important conclusions.\n\nDespite its weaknesses, I recommend acceptance.", "weaknesses": "I think the biggest weakness of the paper lies in the presentation.\n\nI think Section 3 was beautiful.  It flowed naturally, the experiments were clean, and the authors did a great job of pulling out crisp conclusions.\n\nSection 4 was fine; it introduces NPSQ. It would have been nice to connect the mathematical notation in Section 3 to that in Section 4 a bit more directly -- it was VERY unclear what the \"score\" function in Section 3 was -- and since it was mentioned that \"log p(x|q,c)\" was part of it, it seems like there are some unstated notational overlaps between the sections.\n\nWhere things get muddled is Section 5.  I supposed I expected to see a clean comparison of NPSQ vs. the vanilla Choice Sensitivity in Section 3, but instead, the authors *also* introduced the idea of adversarial prompting.  This came out of nowhere, and (to me, at least) derailed the narrative flow.  I kind of see how it was designed to really skew the choice information, and therefore demonstrate how NPSQ was more robust the regular CS, but it was pretty unclear to me why this idea was introduced in Section 5 -- it seems like it should have been introduced earlier, or not at all.\n\nI of course wish that the authors had tested on a wider variety of LLMs.  I understand computational limitations and all, but still - it seems that, as a benchmarking paper that is entirely empirical, more could have been done.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3kPDCDhW7B", "reviewer": "Reviewer_ACim", "summary": "This paper argues that standard MCQA metrics reward models for exploiting \"option-surface cues\" rather than understanding the question. It proposes NPSQ (Normalized Probability Shift by the Question), a new metric that isolates the question's contribution by comparing an option's log-likelihood with versus without the question, then normalizing this gain. Experiments on HellaSwag, ARC-Challenge, and MMLU show that while standard accuracy is fragile to adversarial or rephrased choices, NPSQ accuracy remains stable and can reorder model rankings. The study also quantifies this \"choice sensitivity\" and explores other factors like formats and prompts.", "questions": "See Weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper identifies and formalizes a pervasive evaluation artifact, choice sensitivity, and gives a principled, testable metric to mitigate it.\n\n2. The core construct (question-conditioned vs. question-ablated likelihood shift with normalization) is simple, auditable, and easy to slot into existing LM-eval pipelines.", "weaknesses": "1. The normalization in NPSQ is not stress-tested against plausible alternatives (e.g., z-scores, temperature scaling, ECE), leaving ranking stability under-substantiated.\n\n2. Key stability claims (flip rates, adversarial drops) lack uncertainty quantification and significance testing, weakening the statistical support for the conclusions.\n\n3. The metric relies on token-level probabilities and a hand-crafted “no-question” template whose wording or API backend may change outcomes, reducing reproducibility.\n\n4. Computing scores with and without the question doubles evaluation cost, which is non-trivial for large suites.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Cque0dlgqj", "reviewer": "Reviewer_yhhk", "summary": "This paper investigates \"choice sensitivity\" in the MCQA evaluation of LLMs: the phenomenon where models exploit superficial features of the answer options rather than genuinely comprehending the question. The authors demonstrate that a significant portion of model decisions (20-60%) is driven by this sensitivity. To address this, they propose a new evaluation metric, Normalized Probability Shift by the Question (NPSQ), which is designed to isolate the impact of the question itself. Experiments, particularly those using adversarial choices, show that NPSQ is significantly more robust to option-based artifacts than traditional log-likelihood or length-normalized metrics.", "questions": "N.A.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper addresses a clear and increasingly important issue in LLM evaluation. As models achieve high scores on benchmarks, it is crucial to understand if this reflects true comprehension or artifact exploitation.\n\nThe proposed NPSQ metric is intuitive, well-motivated, and directly targets the identified problem by quantifying the \"value\" of the question.\n\nThe use of adversarial choices provides a very clear and convincing demonstration of the weaknesses of existing metrics and the robustness of NPSQ. The results in Figure 3 are particularly compelling.", "weaknesses": "While valuable, the contribution is an incremental improvement in evaluation methodology rather than a new task, model, and with no fundamental insight into model reasoning.\n\nThe analysis of why models exhibit this sensitivity is not deeply explored, though this is not the primary focus. The experiments are solid but could be extended to a wider range of model architectures and benchmarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KWNSrw369z", "reviewer": "Reviewer_WE2p", "summary": "In this paper, authors raise the issue of LLM answers to questions with option choice being driven by some intrinsic hints in the option texts, not by understanding of the question. Authors decompose the log-probability that model assign to each answer to the given question into choice- and question-driven components and develop a method to identify questions for which the model's answers are caused by choice texts. Authors apply this method to analyze several LLMs (base and instruction-tuned) on three common MCQA benchmarks. Finally, they propose a new method to select the answer option (NPSQ) that is less sensitive to superficial characteristics of the answer choices than log-probability and provide analysis to show that it is a more reliable tool to assess the model's comprehension of the question.", "questions": "- Would inclusion of irrelevant (i.e., ) \"uncertainty sinks\" options (like ``I don't know`` or ``None of the above``) affect the model's sensitivity to options?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- This paper addresses an important problem; it proposes a novel approach to identify and mitigate such \"choice-induced\" biases; this method is easy to deploy and does not require training of a model or additional data.\n\n- The proposed method can be used to improve existing (and future) MCQA benchmarks by removing \"shortcuts\" presented in the formulation of choice option texts.\n\n- Experiments cover different task formulations (options only/cloze prompting/hybrid). A detailed analysis of the proposed method (NPSQ) and observed effects is provided.\n\n- Paper is well-written and easy to follow.", "weaknesses": "- My main concern regarding the proposed method is the fact that it does not take into account effects caused by the order of the options (which is known from the previous studies in the field to be an influential factor). In several works it was shown that, given a list, LLMs often `focuses' more on the later entries from it than on earlier. It can (theoretically) affect the proposed sensitivity analysis method:\n\nChoice sensitivity inequality for one question (line 140) can be reformulated into\n$$\n2 * (Score_{choice}(Q, C, x_1) - Score_{choice}(Q, C, x_2) ) > Score(Q, C, x_1) - Score(Q, C, x_2)\n$$\nif $x_1$ is the last option (e.g., D for 4-option setup in MMLU), and $x_2$ is one of the first, left side may be very high, because without question (empty \"C\" in calculation of $Score_{choice}$) mentioned above effect of list item recall may take place.\n\nA similar issue arises in the formula for NPSQ (line 321).\n\n I would recommend to further analyze this issue and, maybe, (although it is just one of the potential ways to address it, if it is a real problem; I do not insist on using this suggestion) average probabilities from initial options order and after a certain permutation.\n\n- The performed experiments cover only relatively small models up to 8B parameters. Presented results show that larger model has smaller (but still noticeable) sensitivity bias, and it would be very interesting to see the results for a larger model (at least -14B or -32B).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)