Papers by Erik Wilson

1 papers
Conservative Bias in Large Language Models: Measuring Relation Predictions (2025.findings-acl)

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Challenge: Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, often defaulting to no_relation label when an appropriate option is unavailable.
Approach: They systematically evaluate the trade-off between conservative bias and hallucination in relation extraction tasks by using SBERT and LLM prompts to quantify this effect.
Outcome: The proposed model defaults to no_relation label twice as often as hallucination, resulting in significant information loss when reasoning is not explicitly included in the output.

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