ContextGuard: Structured Self-Auditing for Context Learning in Language Models
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Computer Science > Computation and Language
Title:ContextGuard: Structured Self-Auditing for Context Learning in Language Models
Abstract:Recent benchmarks reveal that despite strong reasoning capabilities, large language models (LLMs) still struggle to faithfully apply complex contextual knowledge. These failures are often not wholesale reasoning collapses: in context-rich tasks, models may follow the central reasoning path while missing peripheral, persistent, or format-sensitive requirements.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.26827 [cs.CL] |
| (or arXiv:2605.26827v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26827
arXiv-issued DOI via DataCite (pending registration)
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