Enterprise AI Analysis
CITE-WHILE-YOU-GENERATE: TRAINING-FREE EVIDENCE ATTRIBUTION FOR MULTI-MODAL CLINICAL SUMMARIZATION
This paper introduces 'Cite-While-You-Generate', a training-free framework for real-time evidence attribution in multimodal clinical summarization. Leveraging decoder attention, the method directly cites supporting text spans or images, overcoming limitations of prior post-hoc or retraining-based approaches. It offers two multimodal strategies: raw image mode (direct image patch attentions) and caption-as-span mode (using generated captions for text-based alignment). Evaluated on CLICONSUMMATION and MIMIC-CXR datasets, the framework consistently outperforms embedding-based and self-attribution baselines, achieving significant improvements in both text-level and multimodal attribution accuracy (e.g., +15% F1 over embedding baselines). The caption-based approach is highlighted as a lightweight, practical, and competitive alternative. These findings underscore the potential of attention-guided attribution to enhance the interpretability and deployability of clinical summarization systems, a crucial step towards trustworthy AI in healthcare.
Executive Impact at a Glance
Our analysis reveals the core value proposition and tangible benefits for your enterprise:
F1 Improvement over embedding baselines
Macro-F1 (Text-only Attribution)
Joint EM (Caption-based multimodal)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Multimodal Strategies
Performance & Robustness
Hyperparameter Optimization
Enterprise Process Flow
Chunking (Segment source into evidence units)
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Attention Pooling (Average across layers/heads)
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Mapping & Aggregation (Token-to-sentence citations with threshold)
The framework utilizes a three-stage pipeline to transform noisy, token-level attention weights into stable and interpretable citations. This process ensures faithful mapping of generated statements to underlying evidence, supporting both text-only and multimodal inputs.
| Feature | IMG_RAW Mode | IMG_CAP Mode |
|---|---|---|
| Description | Directly uses image patch attentions to link output tokens to visual evidence. | Replaces image placeholders with model-generated captions, enabling purely text-based alignment. |
| Fidelity | Stronger alignment with raw visual data. | Preserves a textual trace to visual evidence, good for text-based alignment. |
| Performance (Text F1) | Generally stronger text grounding (e.g., 65.52 F1 for Qwen2.5-VL). | Competitive text F1, slightly lower than IMG_RAW. |
| Performance (Joint EM) | Weaker joint attribution. | Higher joint exact match (e.g., 34.87 for Qwen2.5-VL). |
| Complexity | More memory-intensive, potentially less coherent across modalities. | More lightweight and practical, attractive for privacy concerns. |
| Best Use Case | When high-fidelity visual grounding is paramount and resources allow. | When images cannot be shared directly or efficiency/privacy are key concerns. |
The paper explores two complementary strategies for multimodal attribution, balancing direct fidelity to visual evidence against efficiency and text-only compatibility. Each approach offers distinct advantages for integrating visual information into the summarization process.
76.33%
Macro-F1 (Qwen2.5-VL Text-only attribution)
Our attention-guided framework consistently outperforms strong baselines across both text-only and multimodal summarization tasks. This highlights its robustness and effectiveness in generating reliable source citations.
A detailed ablation study revealed critical insights into the design choices that govern attribution stability. Majority voting over top-k attentions is crucial for converting noisy token-level signals into stable, interpretable sentence-level citations, significantly outperforming single-strongest token selection. Moderate aggregation thresholds balance recall and precision, with optimal settings identified for robust performance.
- Majority voting is essential for converting noisy token-level attentions into stable sentence-level attributions, yielding 60+ point gains in F1 over single-token selection.
- Optimal hyperparameters were identified as
k=3(top-k tokens) andτ=0.16(aggregation threshold), providing the best trade-off between robustness and stability. - Attention-guided methods consistently outperform embedding and self-attribution baselines across both models and modalities, confirming the strength of this approach.
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Your Enterprise AI Implementation Roadmap
Our structured approach ensures a smooth transition and measurable impact within your organization.
Phase 1: Foundation Setup
Configure existing MLLMs (Qwen2.5-VL, LLaVA-NeXT) to enable attention output. Implement the three-stage attribution pipeline (chunking, attention pooling, mapping/aggregation).
Phase 2: Multimodal Integration
Integrate raw image attribution mode (direct patch attention) and caption-as-span mode (model-generated captions). Define image token blocks and captioning mechanisms.
Phase 3: Hyperparameter Tuning
Conduct ablation studies on top-k tokens, attribution mode (max vs. majority), and aggregation threshold (τ) to optimize for stability and accuracy, using text-only splits for isolation.
Phase 4: Evaluation & Refinement
Evaluate against embedding-based and self-attribution baselines across CLICONSUMMATION and MIMIC-CXR. Refine pipeline based on macro-F1, exact match, and image accuracy metrics.
Phase 5: Deployment & Monitoring
Integrate into clinical summarization workflows for real-time, interpretable summaries with citations. Establish monitoring for faithfulness and clinical utility in production environments, ensuring continuous improvement based on clinician feedback.
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