Can Hallucinations Be Useful? Solving Multi-Hop Questions With SLMs By Chaining System-I/II Reasoning

Researchers have found that hallucinations in small language models (SLMs) can be useful for solving complex multi-step reasoning problems. A new study proposes a "answer first-reason later" framework that allows SLMs to quickly generate initial answers before retrieving evidence for deeper reasoning, outperforming traditional "think-first" approaches on multi-hop question-answering benchmarks.

arXiv:2605.27596v1 Announce Type: new Abstract: Recently, there has been increased interest in Small Language Models SLMs , which are fast, show good performance, and have lower hardware demands than large language models LLMs . However, SLMs hallucinate more frequently than LLMs, impacting their ability to solve complex multi-step reasoning problems as early mistakes cascade to the final response. To address this, existing works think-first followed by iterative retrieval to reduce hallucination. We argue that the think-first strategy is not always necessary as we find that: i SLMs are often accurately confident in their initial answer and, ii hallucinations can actually be beneficial for honing in on the true answer. As such, we position our work as an inversion of this strategy, i.e., answer first-reason later. We propose a cognitively-inspired framework where the model is first allowed to quickly answer the question System-I zero-shot and then resorts to deeper thinking System-II based on evidence retrieved from a knowledge source using the initial hypothesis. By combining System-I and System-II style thinking, we show that our method can outperform prior work that takes the traditional think-first route on various multi-step question-answering benchmarks.