arXiv — NLP / Computation & Language · · 4 min read

Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4

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Computer Science > Computation and Language

arXiv:2609.30716 (cs)
[Submitted on 25 Sep 2026]

Title:Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4

Authors:Amanda Fitch
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Abstract:When a language model receives two conflicting documents as input, how does it decide which one to prioritize? Does it rely on how the sources are framed or the presentation order of the documents? We evaluated this behavior on Google's pre-trained Gemma 4-e4b model across a targeted behavioral suite (n = 13 items, 784 forward passes in short, single-turn contexts) using a completely counterbalanced experimental design. This setup allowed us to mathematically isolate the specific effects of source framing and reading position, while ensuring the model's natural vocabulary biases were canceled out.
Across ten test conditions, we discovered the following:
1. Source framing heavily overpowers reading position. When directly competing, the semantic framing of a source (such as presenting it as an official guideline or a fresh update) had a significantly stronger impact on the model's final answer than the presentation order of the document.
2. The model favors the first document it reads, but this bias is highly variable. While the model consistently demonstrated a primacy effect (preferring the first document presented), the actual strength of this bias fluctuated by at least a factor of 5 based solely on the surface wording.
3. Overall structural repetition, not short copy-cues, drives positional bias. The model's preference for the first document is not a mechanical reaction to short, repetitive trigger phrases, such as "is [Answer]". However, the primacy effect does increase significantly when the two competing documents are structurally identical, using word-for-word verbatim templates. Introducing variation in the overall wording between the two sources reduces this positional bias.
Comments: 36 pages, 1 figure, evaluation dataset and logs released
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.30716 [cs.CL]
  (or arXiv:2609.30716v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.30716
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Amanda Fitch [view email]
[v1] Fri, 25 Sep 2026 02:43:40 UTC (94 KB)
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