The Illusion of Novelty: Why LLMs Struggle with Scientific Research Questions

Large Language Models (LLMs) are increasingly hailed as tools capable of generating and evaluating scientific ideas. But can they truly assess novelty? A recent study suggests otherwise. Researchers introduced RQ-Bench, a benchmark derived from arXiv publications, to test LLMs on their ability to assess the novelty of research questions (RQs).

The RQ-Bench Initiative

RQ-Bench is built on the backbone of recent academic papers. For each paper, researchers identified the author's core questions. These RQs serve as reference points to test how models judge novelty. Notably, these aren't the only valid questions, just anchored examples in the context of the paper.

Evaluators employed both standalone and comparative LLM judging, alongside human experts, to assess these questions. The results? LLMs consistently rated model-generated RQs as highly novel. But is this novelty real?

A Novelty Mirage?

Here's what the benchmarks actually show: LLMs create a 'novelty mirage.' When LLMs are compared against each other, this illusion intensifies. However, human experts typically prefer the original author-anchored questions. Why? Perhaps because they recognize subtleties in context and significance that LLMs miss.

And the numbers tell a different story too. Many generated RQs are criticized for being too narrow or too closely tied to their source material. Yet, LLMs often overlook this unless specifically challenged to consider it. So, can we really trust models to evaluate the novelty of scientific questions?

Implications for Scientific Review

This discrepancy between LLMs and human experts highlights a serious concern. If models are used to judge scientific novelty, decisions may lean toward perceived novelty rather than grounded innovation. The architecture matters more than the parameter count understanding the context.

Frankly, the reliance on LLMs for novelty evaluation might be premature. This raises a pointed question: are we ready to entrust AI with the intricacies of scientific judgment?

In the end, while LLMs offer impressive capabilities, they're not infallible. For accurate novelty assessment, human expertise remains indispensable.