Racing Thoughts: Explaining Contextualization Errors in Large Language Models (2025.naacl-long)
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| Challenge: | Large Language Models have demonstrated a remarkable capacity for accomplishing a wide variety of language generation and classification tasks. |
| Approach: | They propose a race conditions hypothesis to explain contextualization errors . they propose to use a variety of techniques to test the hypothesis . |
| Outcome: | The proposed model fails to properly contextualize a financial institution if it does not include a bank . the proposed model is based on the race conditions hypothesis . |
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