arXiv:2605.29223v1 Announce Type: new Abstract: The parameter counts of the most widely used large language models (LLMs) are often withheld by their developers, leaving model size -- a primary reference point for interpreting capabilities and costs -- largely undisclosed. We propose a black-box method to infer conservative lower bounds on LLM size from generated text outputs alone, requiring nothing beyond the ability to submit text fragments and observe next-token predictions. Our approach is grounded in a key observation: popular, widely-circulated texts -- such as classical literature, rel
The increasing opacity around LLM development and the strategic importance of model size for various applications make this inference method particularly timely.
This development enables external actors to independently assess and benchmark proprietary LLMs, fostering greater transparency and potentially influencing competitive dynamics.
The ability to infer LLM size from black-box interactions shifts intelligence gathering around AI capabilities from developer disclosure to independent verification techniques.
- · AI researchers
- · Competitive intelligence firms
- · Academics
- · Open-source AI
- · Proprietary LLM developers seeking full opacity
- · Less efficient LLMs
Increased transparency regarding LLM capabilities will allow for more informed purchasing and deployment decisions.
Public pressure may mount on developers to disclose more technical details, potentially driving more standardized reporting.
The inferred size could become a key factor in geopolitical assessments of AI power, especially concerning models used for sensitive applications.
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Read at arXiv cs.LG