Generalization Bias in Large Language Model Summarization of Scientific Research
Uwe Peters, Benjamin Chin-Yee
If you're building AI summarization for research, science, or medical content, add a constraint-preservation layer. Force the model to extract and surface limitations before generating summaries. Test outputs against original abstracts for scope creep.
LLMs summarizing research papers strip out caveats and scope limitations, turning "this worked in lab mice" into "this works." Readers get confident conclusions without the constraints.
Method: Tested 10 LLMs including ChatGPT-4o and Claude-3.5-Sonnet on scientific abstracts. The models systematically omitted methodological constraints—sample sizes, population specifics, controlled conditions—that limit generalizability. When asked to summarize findings, LLMs produced broader claims than the original studies warranted. The pattern held across models: they preserved the headline result but dropped the footnotes that matter.
Caveats: Study focused on abstracts, not full papers. Real-world summarization often involves longer, more complex documents where omissions may differ.
Reflections: Can fine-tuning or prompt engineering reliably preserve methodological constraints without sacrificing readability? · Do users actually notice or care about missing caveats when reading AI-generated summaries? · How does this generalization bias compound when LLMs summarize summaries?