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A benchmark of probabilistic inference found that many tested language models favored a fixed “Yes” or “No” response instead of consistently following the inference. The result raises a practical question: is a model reasoning through the problem, or defaulting to an answer? The evidence comes from a specific, zero-shot, English-language benchmark—not a finding about every model or every kind of reasoning. The benchmark has been identified as “Benchmarking LLM Competence on Logical Inference over Probability Operators,” linked to arXiv:2607.27405; the connection between that work and the supplied title “Fixed-Answer Bias Emerges Before LLM Reasoning” has not been established.
What the benchmark reports
The related benchmark tested probabilistic inference using 15 templates and 14,320 prompts. It varied question form, polarity, and surface content while keeping the underlying logical forms fixed, then evaluated 29 models. The authors report that most models showed a fixed Yes/No preference rather than reliably matching their answer to the inference. Only 9 of the 29 exceeded a random-competence baseline.
These figures describe that benchmark and its model set; they should not be read as a general pass rate for language models across tasks.
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Why a fixed answer preference matters
Accuracy alone can conceal a response habit. If a test contains enough items whose correct answers align with a model’s favored response, the model may score respectably without reliably separating supported conclusions from unsupported ones. A baseline that accounts for fixed responses helps distinguish that pattern from competence at the inference task.
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This distinction is useful when assessing claims about reasoning: a correct answer on one item does not show that the model derived it from the premises. The benchmark’s reported pattern is evidence about performance on its task family, not proof that all apparent reasoning by all LLMs is merely answer bias.
Question wording changed reported accuracy
The benchmark reports accuracy differences of up to 64 percentage points for semantically identical questions expressed using different negation strategies. That result suggests evaluation wording can materially affect measured performance, even when the intended meaning stays the same.
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For readers comparing model results, the wording and polarity of prompts matter alongside the aggregate score. A result from one phrasing should not automatically be treated as evidence that the model will handle an equivalent question phrased another way.
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- Scope: The evaluation is described as zero-shot and English-only. It does not establish equivalent behavior in other languages or under different prompting strategies.
- Answer format: The researchers parsed the first answer token rather than evaluating a free-form justification. The result therefore concerns answer selection in that setup, not the quality of extended reasoning.
- Generalization: The work leaves longer generation and other languages for future investigation. Its findings apply to the benchmark and task family described, not every form of LLM reasoning.
How to compare findings about answer bias
A useful comparison should look beyond headline accuracy. Check whether the evaluation measures a fixed-response preference, tests competence against a baseline that accounts for such responses, varies wording and negation, and covers the tasks you care about. Also check whether it scores a first-token answer or assesses a free-form explanation: those formats measure different things.
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The available description supports those distinctions for this benchmark, but it does not establish a complete model-by-model comparison across all these dimensions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which paper is tied to this title?
The exact title “Fixed-Answer Bias Emerges Before LLM Reasoning” could not be matched to a paper in the available material. The closest relevant result is a secondary article titled “Benchmarking LLM Competence on Logical Inference over Probability Operators,” which links to arXiv:2607.27405. The available evidence does not confirm that this is the work intended by the supplied title, so the reported numbers above are attributed to the related benchmark rather than to a paper with that exact title.
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