Query Circuits: Explaining How Language Models Answer User Prompts
📰 ArXiv cs.AI
Learn how Query Circuits explain language model outputs for specific user prompts, improving transparency and understanding of AI decision-making.
Action Steps
- Apply Query Circuits to a language model to trace information flow for a specific input query
- Run experiments to compare the performance of different language models using Query Circuits
- Configure Query Circuits to analyze the output of a language model for a particular task, such as sentiment analysis or question answering
- Test the effectiveness of Query Circuits in explaining language model outputs for various input queries
- Compare the results of Query Circuits with other explanation methods, such as surrogate-based approaches
Who Needs to Know This
NLP engineers, AI researchers, and data scientists can benefit from Query Circuits to analyze and improve language model performance, while product managers and entrepreneurs can utilize this knowledge to develop more transparent AI-powered products.
Key Insight
💡 Query Circuits provide local, input-level explanations for language model outputs, improving transparency and understanding of AI decision-making.
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🤖 Introducing Query Circuits: a new method to explain how language models answer user prompts! 📊
Full Article
Title: Query Circuits: Explaining How Language Models Answer User Prompts
Abstract:
arXiv:2509.24808v2 Announce Type: replace Abstract: Explaining why a language model produces a particular output requires local, input-level explanations. Existing methods uncover global capability circuits (e.g., indirect object identification), but not why the model answers a specific input query in a particular way. We introduce query circuits, which directly trace the information flow inside a model that maps a specific input to the output. Unlike surrogate-based approaches (e.g., sparse aut
Abstract:
arXiv:2509.24808v2 Announce Type: replace Abstract: Explaining why a language model produces a particular output requires local, input-level explanations. Existing methods uncover global capability circuits (e.g., indirect object identification), but not why the model answers a specific input query in a particular way. We introduce query circuits, which directly trace the information flow inside a model that maps a specific input to the output. Unlike surrogate-based approaches (e.g., sparse aut
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