The recent surge of language models has rapidly expanded NLP research, driving an exponential rise in submissions and acceptances at major conferences. Yet this growth has been shadowed by escalating concerns over conference quality, e.g., plagiarism, reviewer inexperience and collusive bidding. However, existing studies rely largely on qualitative accounts (e.g., expert interviews, social media discussions, etc.), lacking longitudinal empirical evidence. To fill this gap, we conduct a ten year empirical study spanning seven leading conferences. We build a four dimensional bibliometric framework covering conference scale, core citation statistics,impact dispersion, cross venue and journal influence, etc. Notably, we further propose a metric Quality Quantity Elasticity, which measures the elasticity of citation growth relative to acceptance growth. Our findings show that ML venues sustain dominant and stable impact, NLP venues undergo widening stratification with mixed expansion efficiency, and AI venues exhibit structural decline. This study provides the first decade-long, cross-venue empirical evidence on the evolution of major conferences.

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