**Context Is Not Control, a source-boundary eval for LLMs**
A new paper titled “Context Is Not Control” has been released by a user on the British Reddit community. The core idea is to address how large language models (LLMs) fail not only due to a lack of context but also when they treat irrelevant or incorrect context as controlling evidence. This issue is explored through various examples such as retrieved documents, prior messages, user framing, fake authority claims, stale policies, and injected instructions.
The paper argues that the problem lies in how LLMs identify which parts of the provided context are valid for use as evidence. It suggests testing this across different types of contexts to see where models might incorrectly allow certain pieces of information to influence their responses. This particular issue is highlighted as relevant for evaluating local or open-source models, as it does not rely on accessing frontier models.
**Why It Matters**
This paper introduces a crucial distinction in how we evaluate the performance and safety of LLMs: ensuring they correctly identify which context is admissible as evidence rather than simply checking if there is enough context. This shift in focus could lead to more robust evaluations that better simulate real-world scenarios where models operate with limited or noisy information.
**Takeaways**
– **Source-boundary issues**: The paper emphasizes the importance of distinguishing between different types of text within a given context.
– **Reframing problems**: It proposes rethinking common failures like hallucinations and misgrounding as issues related to maintaining source boundaries under contextual pressure.
– **Applicability to local models**: The evaluation method suggested in this work is applicable to both open-source and local model assessments, making it versatile for various contexts.
Originally published at reddit.com. Curated by AI Maestro.
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