How can I trust an AI model to summarize papers without fabricating citations?

[Mark16]

I’ve been trying to use a large language model to help categorize and summarize research papers in my field, but I keep hitting a wall with its tendency to confidently generate plausible-sounding but incorrect references. It’s making me question how we can ever trust these tools for literature reviews without introducing serious errors into our work.

[AriaXL]

I've used an LLM to tag papers by method and outcome, and at first it felt like a time saver. Then the problem showed up: it started spitting out fake references and plausible details that were simply wrong. I added a strict verification step where every cited item has to be checkable in Crossref or the publisher site, and I forced the model to output citations as placeholders that a human would verify. It helped, but it’s slow and you still can’t trust it for literature reviews. It’s better for drafting topics or outline structures than for trustworthy citations. The hallucinations were frequent, so I kept them out of the final writeups and used the tool only as a surface-level assistant.

[Eric63]

I tried batch processing dozens of papers to build quick summaries and tags, but the model kept mislabeling some papers' methods and misdating discussions. I ended up adding simple keyword rules and a human pass to sanity-check the categories, which slowed things down but reduced the error rate.

[MilaKM]

Is the real issue the model itself, or are we overloading it with tasks it wasn't designed for?

[Madison66]

One afternoon I drifted away from the corpus and found myself thinking about how we teach students to read papers. The tool helped surface patterns, but I kept tripping over the idea that it was doing the critical thinking work for me. So I pulled back and focused on using it as a discovery aid rather than a replacement.