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Effective multi-domain adaptation of large language models requires prioritizing relevant domain knowledge while minimizing interference from irrelevant domains. Existing approaches commonly use Low-Rank Adaptation (LoRA) to train a separate adapter for each domain and combine these adapters within one model. However,...
Fact-Ablated Evaluation (FAE) is introduced, a new evaluation framework that iteratively ablates the cited evidence to assess whether LLMs revise their predictions accordingly and highlights that strong fact-checking performance can still coexist with weak evidence dependency, while REAL encourages veracity predictions...
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