The deployment of small language models (SLMs) at the edge raises a fundamental behavioral question: under a fixed word budget, how do these models prioritize semantic content? This paper investigates content selection in two representative edge SLMs—gemma3n-e4b and Llama-3.2-3B—under systematic word-budget compression when generating person descriptions in English and Thai. A 2 × 2 (model × language) factorial design yielded 9360 observations across 18 profiles, 13 budget levels (1–25 words, odd-step intervals), and 10 trials per cell. Output richness was quantified via an Information Density Score (IDS), a binary annotation-based metric capturing seven attributes: name, occupation, gender, education, income, and two personality traits. Results confirm strong, generally increasing IDS–budget relationships across all conditions (Pearson r = 0.663–0.903). In English, gemma3n-e4b reached every key threshold exactly four words ahead of Llama-3.2-3B (B50: w = 7 vs. 11; B70: w = 13 vs. 17; B80: w = 17 vs. 21). In Thai, both models converged at B50 (w = 9), but gemma retained a four-word lead at B70 (w = 13 vs. 17), while Llama never reached B80 (max IDS = 0.796 at w = 25). Occupation was the invariant anchor across all conditions (retention ≥ 0.969). Models diverged on secondary attributes: gemma suppressed Gender (4.8% English, 24.2% Thai), while Llama deprioritized Education (23.6% English, 15.2% Thai; 0.267 at w = 25 in Thai). In Thai, gemma’s personality traits overtook Income in the salience hierarchy—a reordering absent in English. Gemma showed 2.5–3.8× higher within-trial consistency than Llama in both languages. These findings indicate that content selection under budget pressure is determined primarily by model architecture rather than linguistic context, and that Thai reduces but does not eliminate the cross-architecture compression efficiency gap.
Lertyosbordin et al. (Mon,) studied this question.