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About this paper
Michael J. Zellinger, Rex Liu, Matt Thomson
The authors add early abstention to language model cascades, allowing an earlier model to stop a query rather than defer it to the final model. They optimize routing thresholds for errors, expected cost, and abstention preferences. Across six benchmarks, early abstention reduces preference-averaged overall cascade loss by 2.2% on average, but worsens TriviaQA by 2.0%; results depend on user preferences.
Key insights
- Smaller, earlier models in an LLM cascade can effectively predict the abstention decisions of larger, more expensive downstream models, allowing for proactive resource savings.
- Integrating abstention directly into the cascade's internal decision-making (early abstention) is more efficient than a simple post-processing abstention step by the final model.
- Optimizing for a composite cascade loss function, which explicitly includes cost and abstention sensitivities, enables the system to achieve better trade-offs tailored to specific user priorities.
Summary by alphaXiv.
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