Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline (2025.emnlp-main)
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| Challenge: | Multilingual large language models (LLMs) exhibit factual inconsistencies across languages . authors identify two primary sources of error: insufficient engagement of reliable English-centric mechanism for factual recall, and incorrect translation from English back into the target language for the final answer. |
| Approach: | They propose two vector interventions to redirect the model toward better internal paths for higher factual consistency. |
| Outcome: | The proposed interventions increase the recall accuracy by over 35 percent for the lowest-performing language. |
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