mlsanityData to 7 Oct 2026

Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA

arXiv 2606.07548 ↗First posted 5 May 2026cs.IR cs.AI cs.CLcited by 1

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

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 0.720. This result dramatically outperformed a baseline prompt which scored only 0.565. Remarkably, this performance on the efficient Gemini 2.0 Flash was almost identical to the result from the next-generation Gemini 2.5 Flash. Our findings demonstrate that sophisticated prompt design is a critical factor for unlocking the full reasoning capabilities of modern LLMs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 4 of its 14 references that are in the map. First posted on arXiv 5 May 2026.

Builds on

4 of 14 references.

References · 4

  1. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  2. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  3. 2025Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoningcited by 82
  4. 2025On the generalization of language models from in-context learning and finetuning: a controlled studycited by 39

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs
  2. 2024Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations
  3. 2025Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers
  4. 2025Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs
  5. 2026When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models
  6. 2025Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs
  7. 2023Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
  8. 2024OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models