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Truth Behind the Scene: Designing Evaluations Benchmarks to Assess LLMs’ Task-Specific Understanding over Test-Taking Strategies

AAAI 2025 · Large language models › Language, safety and interpretability · approach: Statistical frameworks for LLM evaluation · 0 citations (OpenAlex)

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Proposes two adversarial prompting methods (adversarial counterfactual a-CF and adversarial chain-of-thought a-CoT) to expose LLM test-taking strategies on MMLU-style benchmarks, without experiments.

Problem

Existing benchmarks such as MMLU can be solved via statistical patterns, lexical cues, and memorization rather than genuine task understanding, so they overstate LLM reasoning ability.

Approach

Two prompting-based adversarial evaluation methods: a-CF adds an instruction that pushes models toward surface cues (e.g., longest answer) to see if accuracy drops, and a-CoT embeds adversarial reasoning steps inside chain-of-thought prompts and perturbs surface details of problems to test whether reasoning generalizes.

Key results

• No experiments are reported; the paper proposes a-CF and a-CoT prompt templates and hypotheses about expected performance gaps.
• a-CF uses a single adversarial instruction (favor longest/most detailed answer, avoid domain knowledge) applicable across all 57 MMLU subjects.
• The authors state both methods are not fully tested and are purely empirical proposals due to resource constraints.

Limitations

• Both methods are not fully tested or evaluated on any models.
• Only prompting-based proposals; expected performance gaps are hypothetical.
• Limited to different model families and sizes not yet explored due to resource constraints.

Experimental setup

Datasets: MMLU

Baselines: MMLU MMLU-Pro Nvr-X-MMLU

Builds on: MMLU Chain-of-Thought prompting Nvr-X-MMLU MMLU-Pro

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