Learning When to Trust via Selective Context Preference Optimization
Researchers propose a method to help language models distinguish reliable from misleading context, introducing a benchmark and metric to measure selective trust.
- Language models struggle to distinguish between reliable and misleading external context, leading to incorrect answers.
- MIST is a new benchmark with four matched conditions to evaluate selective trust in models.
- SC2W is a metric that counts how often models are misled by incorrect signals.
- The approach balances robustness with usefulness, avoiding the pitfall of ignoring all context.
A new paper introduces a framework to address a critical weakness in language models: their inability to discern when to trust external context. Current models often rely too heavily on signals, even when those signals are misleading, which can degrade performance. The researchers reframe the problem as selective trust and present MIST, a benchmark with four distinct conditions for each reasoning task (clean, misleading, correct-context, and irrelevant-context). Alongside this, they propose SC2W, a metric that quantifies how often a model is misled by incorrect signals. The work highlights a trade-off: models that ignore all context appear robust but become useless when context is actually helpful. The paper suggests that selective trust training could bridge this gap, improving both reliability and utility in real-world applications where context matters.
Provides tools and benchmarks to train models that selectively trust context, improving reliability in real-world applications.
Helps deploy AI systems that are less prone to errors from misleading inputs, reducing operational risks.
Offers insights into advanced training techniques for language models and the importance of selective trust.
Highlights a key challenge in AI reliability and a potential solution to improve trustworthiness.
- Selective trust
- A model's ability to decide whether to rely on external context based on its reliability.
- MIST
- A benchmark dataset designed to evaluate a model's selective trust under different context conditions.
- SC2W
- A metric that measures how often a model is misled by incorrect or irrelevant context.
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