Analogical reasoning increases LLM solution diversity by 90-173% and novelty rate to over 50%, delivering up to 13-fold gains on biomedical tasks including perturbation prediction and cell communication.
arXiv preprint arXiv:2505.00047 , year=
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Annotation-anchored training reduces semantic diversity collapse in post-trained language models by a factor of six compared to standard supervised fine-tuning while preserving instruction-following and improving with scale.
Proposes a value-encoding framework to characterize and counter homogenization in LLMs by formalizing it via normativity from queer theory and introducing xeno-reproduction tasks from feminist theory, illustrated with a gender-bias experiment on Claude 3.5 Haiku.
citing papers explorer
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Unlocking LLM Creativity in Science through Analogical Reasoning
Analogical reasoning increases LLM solution diversity by 90-173% and novelty rate to over 50%, delivering up to 13-fold gains on biomedical tasks including perturbation prediction and cell communication.
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Annotations Mitigate Post-Training Mode Collapse
Annotation-anchored training reduces semantic diversity collapse in post-trained language models by a factor of six compared to standard supervised fine-tuning while preserving instruction-following and improving with scale.
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The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety
Proposes a value-encoding framework to characterize and counter homogenization in LLMs by formalizing it via normativity from queer theory and introducing xeno-reproduction tasks from feminist theory, illustrated with a gender-bias experiment on Claude 3.5 Haiku.