mlsanityData to 14 Oct 2026

Advancing AI Research Assistants with Expert-Involved Learning

arXiv 2505.04638 ↗First posted 3 May 2025cs.AI cs.CL cs.IRcited by 0

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear. We introduce ARIEL (AI Research Assistant for Expert-in-the-Loop Learning), an open-source evaluation and optimization framework that pairs a curated multimodal biomedical corpus with expert-vetted tasks to probe two capabilities: full-length article summarization and fine-grained figure interpretation. Using uniform protocols and blinded PhD-level evaluation, we find that state-of-the-art models generate fluent but incomplete summaries, whereas LMMs struggle with detailed visual reasoning. We later observe that prompt engineering and lightweight fine-tuning substantially improve textual coverage, and a compute-scaled inference strategy enhances visual question answering. We build an ARIEL agent that integrates textual and visual cues, and we show it can propose testable mechanistic hypotheses. ARIEL delineates current strengths and limitations of foundation models, and provides a reproducible platform for advancing trustworthy AI in biomedicine.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 36 of its 72 references that are in the map. First posted on arXiv 3 May 2025.

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36 of 72 references.

References · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  7. 2024GPT-4o System Cardcited by 4,588
  8. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  9. 2023Mistral 7Bcited by 3,164
  10. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  11. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  12. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
24 more
  1. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  2. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,417
  3. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  4. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 752
  5. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  6. 2025Accelerating scientific discovery with Co-Scientistcited by 401
  7. 2024MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmarkcited by 372
  8. 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 319
  9. 2023Towards Generalist Biomedical AIcited by 289
  10. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  11. 2024LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contextscited by 245
  12. 2023ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generationcited by 118
  13. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 117
  14. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 102
  15. 2023Benchmarking large language models for biomedical natural language processing applications and recommendationscited by 78
  16. 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 62
  17. 2025SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scalingcited by 54
  18. 2022SQuALITY: Building a Long-Document Summarization Dataset the Hard Waycited by 50
  19. 2025Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Modelscited by 33
  20. 2024Analyzing the Performance of Large Language Models on Code Summarizationcited by 19
  21. 2024HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoningcited by 11
  22. 2025InductionBench: LLMs Fail in the Simplest Complexity Classcited by 9
  23. 2024Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with Chain-of-Problemscited by 7
  24. 2025ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Modelscited by 5

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  7. 2024Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
  8. 2026MedViz: An Agent-based, Visual-guided Research Assistant for Navigating Biomedical Literature