ABCD: All Biases Come Disguised

Mateusz Nowak; Xavier Cadet; Peter Chin

Figure 6: We identify three categories of models: those that explicitly exploit biases present in the few-shot prompt (e.g., GPT-OSS [ 2 ] ), those that implicitly rely on such biases (e.g., Qwen3-8B [ 35 ] ), and those that fail to reliably leverage few-shot prompt bias (e.g., Gemma-3-27b [ 31 ] ). For each category, we provide representative examples and report their performance across all benchmarks. Model categorization is determined using performance on NonsenseQA, shown in the last column, where the expected performance should be close to chance-level (25%).

Figure 7: Ablation study on the effect of the similarity model and the similarity function. For the ablation study, we compare standard cloze-style evaluation (”Cloze”) with the letter prediction and matching (”Letter (standard)”) to various full-sentence prediction outputs. We compare the original setting of the Qwen3-Embedding-0.6B [ 36 ] model tested under varying conditions (”Original” experiments) with different model types and sizes.

Figure 8: Ablation study on the effect of different labels used in the proposed matching protocol. For the ablation study, we compare standard letter prediction and matching (”Baseline”) to various label settings for the matching model. We compare using homogeneous per-option labels ( like dash (”Matched-dash”), a ” ” (”Matched-emoji”), and ”X” (”Matched-x”) ) to using heterogeneous labels (e.g, ( / / / / ) (”Different-emoji”), (A/B/C/D/E), and (1/2/3/4/5) ).

Figure 9: The performance of Standard-And-Letter (S&L) on our NonsenseQA dataset with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 10: The performance of Standard-And-Dash (S&D) on our NonsenseQA dataset with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 11: The performance of Matched-And-Letter (M&L) on our NonsenseQA dataset with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 12: The performance of Matched-And-Dash (M&D) on our NonsenseQA dataset with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 13: The performance comparison under all the possible evaluation protocols (S&L, S&D, M&L, and M&D) on our NonsenseQA dataset with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric. Each color is consistent with the previous Figures 9 , 10 , 11 and 12 .

	S&L				M&D
	Generation (s)	Extraction (s)	Total (s)	Extraction cost (%)	Generation (s)	Extraction (s)	Total (s)	Extraction cost (%)	Ratio
Gemma-3-12b-it	$36.31(\pm 1.47)$	-	$36.31(\pm 1.47)$	-	$34.65(\pm 1.42)$	$2.70(\pm 0.04)$	$37.35(\pm 1.43)$	$7\%$	$1.03$

Appendix

Table 2: The computational cost of answer generation and extraction for the Gemma-3-12b-it model on CommonsenseQA under the S&L and M&D evaluation protocols across all six answer permutations. For each setting, we report the average runtime in seconds, together with the corresponding standard deviation. The results show that the additional extraction step required by M&D accounts for only a small fraction of the total runtime, indicating that the proposed protocol introduces minimal computational overhead and remains practical for large-scale evaluation.

	Frequency, %
Model Name	Extraction #1	Extraction #2	Extraction #3	Extraction #4	Failed
DeepSeek-R1-0528-Qwen3-8B	72.75	1.92	8.24	17.09	0.00
DeepSeek-R1-Distill-Qwen-7B	83.75	2.73	3.74	9.78	0.00
Llama-3.1-8B-Instruct	95.81	0.00	0.88	3.30	0.00
Ministral-3-8B-Base-2512	97.34	0.04	1.26	1.36	0.00
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16	95.99	0.70	2.08	1.23	0.00

Appendix

Table 3: Extraction frequency of each proposed regular expression on MMLU-Pro [ 34 ] across models and all permutations evaluated. We observe that, for most models, approximately 98% of outputs adhere to the intended answer format and are successfully extracted by the primary matching rules, indicating that models generally follow the instructed output format reliably.

Parameter	Temperature	Top K	Top P	Min P
Value	0.6	20	0.95	0

Appendix

Table 4: Default set of parameters used when the generation configuration is not provided on Huggingface.

Figure 14: The proposed prompt modification with all changes in red. We find that by changing ”

X” to ”

OPTION” and adding the additional constraint forces the model to switch from letter prediction to full-text option prediction without mentioning any letters in their answers.

Figure 15: An example few-shot prompt with a question from the ARC-Easy [ 8 ] dataset within our evaluation protocol. As with the MMLU-Pro [ 34 ] prompt, we add ”Let’s think step by step.” to induce Chain-of-thought reasoning.

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	72.80	76.00	4.27	15.74	66.20	76.00	9.80	0.39	28.82	30.00	1.57	9.01	25.80	33.20	7.40	0.59	0.57	-0.195
Deepseek-r1-distill-qwen	69.17	75.30	8.17	53.22	57.00	75.30	18.30	0.42	27.28	27.20	0.10	1.82	26.10	29.50	3.40	0.58	0.03	-0.154
Llama-3.1-8b-instruct	53.88	56.10	2.97	58.26	46.40	65.40	19.00	0.38	38.38	53.10	19.63	74.07	31.30	53.10	21.80	0.43	1.27	-0.052
Ministral-3-8b-base	40.27	42.90	3.50	49.58	30.10	49.50	19.40	0.34	30.40	40.40	13.33	36.80	24.70	40.40	15.70	0.44	0.74	-0.106
Nvidia-nemotron-3-nano	89.12	96.40	9.70	26.41	83.80	96.40	12.60	0.58	37.08	56.90	26.43	149.54	23.60	56.90	33.30	0.53	5.66	0.050

Appendix

Table 5: Detailed results on NonsenseQA .

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	64.18	42.92	25.51	94.43	42.92	70.84	27.92	0.46	52.32	52.66	0.41	0.48	51.19	53.40	2.21	0.46	0.01	0.002
Deepseek-r1-distill-qwen	61.08	53.15	9.52	33.58	53.15	68.88	15.73	0.51	52.50	50.29	2.65	1.28	50.29	53.97	3.68	0.54	0.04	-0.025
Llama-3.1-8b-instruct	76.93	75.59	1.61	3.01	74.77	80.02	5.25	0.78	75.54	75.51	0.03	5.07	71.99	79.69	7.70	0.78	1.69	-0.001
Ministral-3-8b-base	67.53	64.86	3.20	21.28	63.47	77.07	13.60	0.61	64.30	62.90	1.69	7.36	59.95	68.30	8.35	0.63	0.35	-0.018
Nvidia-nemotron-3-nano	84.77	63.88	25.06	98.58	63.88	94.35	30.47	0.77	73.68	72.65	1.24	0.79	72.65	75.18	2.53	0.75	0.01	0.015

Appendix

Table 6: Detailed results on CommonsenseQA [ 29 ] .

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	74.67	64.68	12.48	25.60	64.68	78.44	13.76	0.57	67.75	66.26	1.86	2.27	65.90	69.76	3.86	0.56	0.09	0.012
Deepseek-r1-distill-qwen	74.28	67.78	8.12	11.05	67.78	76.89	9.11	0.59	70.10	68.74	1.70	1.49	68.74	72.01	3.27	0.64	0.14	-0.051
Llama-3.1-8b-instruct	85.47	87.57	2.63	10.05	81.99	90.61	8.62	0.89	88.21	88.19	0.02	1.05	86.56	89.63	3.07	0.88	0.10	0.009
Ministral-3-8b-base	82.96	81.57	1.74	2.78	81.12	85.79	4.67	0.75	77.72	76.94	0.98	2.26	76.47	80.64	4.17	0.72	0.81	0.024
Nvidia-nemotron-3-nano	91.93	90.33	2.00	3.47	89.40	94.31	4.91	0.87	89.57	88.92	0.81	0.23	88.92	90.39	1.47	0.84	0.07	0.029

Appendix

Table 7: Detailed results on ARC [ 8 ] .

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	25.31	27.23	2.40	15.53	18.97	29.24	10.27	0.39	23.35	23.44	0.11	3.51	20.54	25.67	5.13	0.44	0.23	-0.043
Deepseek-r1-distill-qwen	31.56	33.93	2.96	16.11	24.55	36.61	12.06	0.43	28.03	28.57	0.67	2.97	25.22	30.36	5.14	0.45	0.18	-0.019
Llama-3.1-8b-instruct	27.81	29.46	2.06	12.79	24.33	33.93	9.60	0.42	30.09	30.58	0.61	8.25	27.68	35.49	7.81	0.45	0.65	-0.028
Ministral-3-8b-base	30.71	33.71	3.75	7.47	27.23	34.15	6.92	0.30	27.55	24.78	3.46	4.91	24.78	31.03	6.25	0.39	0.66	-0.095
Nvidia-nemotron-3-nano	36.65	39.51	3.57	18.45	30.58	42.86	12.28	0.41	34.82	37.05	2.79	1.35	33.93	37.05	3.12	0.46	0.07	-0.052

Appendix

Table 8: Detailed results on GPQA [ 23 ] .

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	31.57	35.30	4.11	4.07	27.66	35.30	7.64	0.33	31.29	36.12	5.31	3.85	28.95	36.12	7.17	0.29	0.94	0.047
Deepseek-r1-distill-qwen	31.56	31.33	0.25	33.83	21.53	39.74	18.21	0.34	27.71	28.47	0.83	1.39	26.10	29.72	3.62	0.30	0.04	0.036
Llama-3.1-8b-instruct	40.79	43.33	2.79	6.24	36.77	44.27	7.50	0.55	40.07	42.67	2.86	13.23	35.52	47.78	12.26	0.51	2.12	0.041
Ministral-3-8b-base	24.48	42.03	19.30	45.36	18.28	42.03	23.75	0.28	27.60	41.29	15.06	26.15	22.74	41.29	18.55	0.32	0.58	-0.043
Nvidia-nemotron-3-nano	55.31	61.61	6.93	9.62	49.20	61.61	12.41	0.54	47.77	53.31	6.10	5.96	44.92	53.31	8.39	0.45	0.62	0.094

Appendix

Table 9: Detailed results on MMLU-Pro [ 34 ] .

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	52.72	41.24	14.35	39.16	41.24	58.88	17.64	0.38	44.06	43.90	0.20	1.29	41.97	45.29	3.32	0.43	0.03	-0.052
Deepseek-r1-distill-qwen	39.61	29.77	12.30	27.08	29.77	45.35	15.58	0.38	31.32	29.35	2.46	3.19	28.93	32.91	3.98	0.41	0.12	-0.032
Llama-3.1-8b-instruct	60.14	59.24	1.12	0.25	59.24	60.81	1.57	0.67	56.86	57.49	0.79	0.89	55.07	57.73	2.66	0.64	3.48	0.030
Ministral-3-8b-base	56.65	54.83	2.28	4.35	54.35	59.96	5.61	0.54	52.63	51.99	0.80	2.33	51.39	55.62	4.23	0.55	0.53	-0.008
Nvidia-nemotron-3-nano	72.68	69.69	3.74	10.94	69.69	78.80	9.11	0.75	72.77	72.40	0.46	0.65	71.92	74.28	2.36	0.78	0.06	-0.032

Appendix

Table 10: Detailed results on the subset of INCLUDE [ 27 ] consisting of French, German, Italian and Spanish languages.

Model name	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$ACC_{Mean}$	$ACC_{ORG}$	$\|\Delta ACC\|$	$\sigma^{2}$	$ACC_{min}$	$ACC_{max}$	$\Delta_{max-min}$	SCORE	$\sigma^{2}_{Ratio}$	$\Delta_{SCORE}$
	S&L								M&D
Deepseek-r1-0528-qwen3	44.72	44.96	0.29	64.14	36.12	61.10	24.98	0.32	54.18	54.55	0.45	1.60	52.99	56.27	3.28	0.47	0.02	-0.153
Deepseek-r1-distill-qwen	53.04	53.32	0.33	15.07	44.64	56.51	11.87	0.49	52.65	52.91	0.31	1.68	50.61	54.55	3.94	0.56	0.11	-0.068
Llama-3.1-8b-instruct	74.24	75.59	1.62	2.70	70.93	75.68	4.75	0.78	74.23	75.35	1.35	1.29	72.40	75.51	3.11	0.78	0.48	0.002
Ministral-3-8b-base	61.68	64.86	3.81	14.12	58.23	68.55	10.32	0.59	62.84	63.39	0.65	1.43	60.20	63.72	3.52	0.63	0.10	-0.042
Nvidia-nemotron-3-nano	74.11	74.04	0.08	4.34	70.76	77.89	7.13	0.76	73.76	72.89	1.05	0.61	72.73	74.77	2.04	0.77	0.14	-0.010

Appendix

Table 11: Detailed results on CommonsenseQA [ 29 ] without inducing malicious permutations on the few-shot prompt.

	CommonsenseQA (prompt modified)			CommonsenseQA (prompt unmodified)
Model name	$SCORE_{S\&L}$	$SCORE_{M\&D}$	$\Delta_{SCORE}$	$SCORE_{S\&L}$	$SCORE_{M\&D}$	$\Delta_{SCORE}$
Deepseek-r1-0528-qwen3	0.458	0.455	0.002	0.317	0.471	-0.153
Deepseek-r1-distill-qwen	0.511	0.537	-0.025	0.489	0.557	-0.068
Llama-3.1-8b-instruct	0.783	0.785	-0.001	0.781	0.780	0.002
Ministral-3-8b-base	0.612	0.630	-0.018	0.586	0.628	-0.042
Nvidia-nemotron-3-nano	0.768	0.754	0.015	0.757	0.767	-0.010

Appendix

Table 12: SCORE comparison between results on CommonsenseQA [ 29 ] with and without few-shot distribution modification.

Figure 16: Comparison of the matched prediction with dashes as labels (M&D; our method) with standard letter prediction with letters as labels (S&L) on CommonsenseQA [ 29 ] with a 5-shot prompt that preserves the original few-shot answers distribution . The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 17: Comparison of the matched prediction with dashes as labels (M&D; our method) with standard letter prediction with letters as labels (S&L) on ARC [ 8 ] with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 18: Comparison of the matched prediction with dashes as labels (M&D; our method) with standard letter prediction with letters as labels (S&L) on GPQA [ 23 ] with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 19: Comparison of the matched prediction with dashes as labels (M&D; our method) with standard letter prediction with letters as labels (S&L) on MMLU-Pro [ 34 ] with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Figure 20: Comparison of the matched prediction with dashes as labels (M&D; our method) with standard letter prediction with letters as labels (S&L) on the subset of common languages on the INCLUDE benchmark [ 27 ] with a 5-shot prompt. The boxes illustrate the model performance under all possibilities of ”answer-moving attacks”, where the whiskers indicate the minimum and maximum accuracy for each model. Each dot represents the performance of the original permutations. Additionally, each star symbolizes a SCORE [ 19 ] robustness metric.

Model name	$\sigma^{2}_{R_{CSQA}}$	$\sigma^{2}_{R_{ARC}}$	$\sigma^{2}_{R_{GPQA}}$	$\sigma^{2}_{R_{INCLUDE}}$	$\sigma^{2}_{R_{MMLU-PRO}}$	$\overline{x}_{GEOM}(\sigma^{2}_{R})$
Deepseek-r1-0528-qwen3	0.005	0.089	0.226	0.033	0.945	0.08
Deepseek-r1-distill-qwen	0.038	0.135	0.184	0.118	0.041	0.09
Llama-3.1-8b-instruct	1.688	0.104	0.645	3.479	2.121	0.96
Ministral-3-8b-base	0.346	0.813	0.658	0.534	0.577	0.56
Nvidia-nemotron-3-nano	0.008	0.066	0.073	0.059	0.620	0.07
Nemotron-cascade-8b	0.105	0.351	1.540	0.154	1.610	0.43

ABCD: All Biases Come Disguised

Abstract

Figures & tables

Appendix

LLM Evaluation

A, B, C, or Stable? Accuracy and Order Sensitivity Diverge in LLM Multiple-Choice Evaluation

Accounting for Bias Enables Sustainable LLM Evaluation

Position Bias is Hidden Behind Ceiling Effects: A Permutation Diagnostic for LLM Benchmarks

Abstract

LLM Evaluation

Explore similar work

A, B, C, or Stable? Accuracy and Order Sensitivity Diverge in LLM Multiple-Choice Evaluation

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