arXiv:2606.07548v1 Announce Type: cross Abstract: The MedHopQA challenge presents a critical test for Large Language Models (LLMs): complex, multi-hop reasoning in the high-stakes biomedical domain. This paper details our direct API-based evaluation of Google's Gemini Flash models, focusing on the impact of advanced prompt engineering. We designed a sophisticated, multi-component prompt for Gemini 2.0 Flash that combined role-playing, explicit multi-shot Chain-of-Thought (CoT) examples, and detailed formatting rules. Our best run, using this complex prompt, achieved a Concept Level Score of 0.
The continuous development and benchmark-driven evaluation of LLMs for complex reasoning tasks, especially in high-stakes domains like biomedicine, is a constant and current area of AI research.
This highlights the ongoing challenge of achieving reliable, multi-hop reasoning in LLMs even with advanced prompting, particularly in critical applications where accuracy is paramount.
It reinforces the understanding that despite model advancements, sophisticated prompt engineering is still a critical, non-trivial factor for performance, and that current LLMs still struggle significantly with complex factual consistency.
- · Prompt engineers
- · Google (from evaluation and feedback)
- · AI researchers
- · LLMs for 'out-of-the-box' complex reasoning
- · Applications requiring high-stakes, multi-hop accuracy without extensive fine-tu
The paper underscores the current limitations of Gemini Flash, and potentially other LLMs, in performing reliable multi-hop reasoning even with advanced prompting techniques.
This may lead to increased focus on fundamental model architectures that inherently support better reasoning, or hybrid AI systems combining LLMs with symbolic reasoning or knowledge graphs.
The high 'noise' level highlighted by a 0.0 Concept Level Score in a critical domain like biomedicine suggests that fully autonomous medical AI agents requiring complex reasoning are still distant, necessitating human oversight for the foreseeable future.
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Read at arXiv cs.AI