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Despite high retrieval rates, LLMs miss over 47% of relevant studies in meta-analysis, exposing a critical gap in their systematic reasoning capabilities.
ReGrad enables scalable and reversible knowledge injection without the risk of catastrophic forgetting, outperforming traditional methods in both general and domain-specific tasks.
SKIM compresses procedural skills in LLMs by 30-60% without sacrificing performance, revolutionizing how we manage reusable natural language skills.
Current LLM judges show a troubling reliability gap in long-form evaluations, raising questions about their effectiveness in real-world applications.
Untangling task-solving skills from factual knowledge in PRAG adapters makes them play better together, boosting performance when you combine multiple documents.
