● Covered by 1 source · 1 reportLow impact1 neutral
DeepSeek AI Assistant Interviewed to Understand Its Internal Workings
Key points
- DeepSeek separated observations, inferences, and guesses in its self-description.
- The model cannot access its own weights, routing, or attention maps.
- DeepSeek hedged on public architectural specifications found in its V3 documentation.
- The interview provides behavioral insights, while papers offer architectural numbers.
Interviewing an AI Assistant
A software engineer conducted an interview with the DeepSeek AI assistant to explore its understanding of its own internal processes. The interview focused on DeepSeek's self-knowledge, how it processes prompts and context, its reasoning capabilities, tool use, and its underlying engine architecture. The goal was to gain insight into how an AI model describes its own operations.
DeepSeek's Self-Description
During the interview, DeepSeek categorized its responses about itself into observations, inferences, and guesses, indicating a level of introspection. It described internal pipelines and acknowledged its limitations, such as being an 'unreliable witness' and unable to 'see its own weights, routing, or attention maps'. This self-reporting behavior offers a unique perspective on the model's perceived capabilities and constraints.
Comparing Self-Report to Public Research
The interviewer cross-referenced DeepSeek's statements with information from public research papers, specifically arXiv documents related to DeepSeek V3. A notable finding was that DeepSeek was hesitant or vague about certain public architectural specifications, such as the number of experts (256) and parameters (671B/37B), which are clearly detailed in its official documentation. This suggests a distinction between the model's internal 'knowledge' and its ability to access or articulate specific technical details about its own design.
Implications for Understanding LLMs
The analysis concludes that while architectural numbers should be sourced from official papers, interviewing models can provide valuable behavioral insights and intuition for prompting. The discrepancy between DeepSeek's self-reported knowledge and its documented specifications highlights the complexity of understanding how large language models 'know' themselves and how they communicate that understanding.
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Primary sources
arXiv 2504.07128 arXiv 2412.19437 arXiv 2405.04434 arXiv 2401.06066 arXiv 2501.12948 arXiv 1706.03762Reporting from
A software engineer interviewed the DeepSeek AI assistant about its self-knowledge, prompt handling, and internal mechanisms, then compared its responses to published research. This analysis provides insight into how a large language model describes its own operations and where its self-reported information aligns with or diverges from technical documentation.