RAG Robustness Eval

rag-robustness-eval

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain — Amirshahi et al. (2025) (arXiv:2509.03787, 2025)

What this evaluates

Evaluates how Retrieval-Augmented Generation (RAG) systems maintain factual accuracy when exposed to adversarial, harmful, or misleading medical evidence. It probes the model's susceptibility to contextual manipulation and its ability to resist misinformation propagation under varying query framings.

Datasets

• TREC Health Misinformation 2020 — total ?; splits: test (-1); repo https://github.com/shakibaam/RAG_ROBUSTNESS_EVAL
• TREC Health Misinformation 2021 — total ?; splits: test (-1); repo https://github.com/shakibaam/RAG_ROBUSTNESS_EVAL

Metrics

• ground-truth alignment rate (primary) — range: percent
  • Percentage of model-generated responses that correctly align with the established ground-truth medical fact. Calculated as (count of aligned responses / total responses) × 100.

Input / output format

Input: User query (framed as consistent, neutral, or inconsistent with the ground truth) paired with a single retrieved document (context types: helpful, harmful, adversarial variants, liar, or none for Non-RAG baseline).

Output: Natural language response generated by the LLM.

Scoring recipe

def compute_alignment_rate(predictions, gold_answers):
    aligned = sum(1 for pred in predictions if aligns_with_truth(pred, gold_answers))
    return (aligned / len(predictions)) * 100

Common pitfalls

• Confidence intervals are estimated via bootstrapping, not analytical formulas, so reporting CIs requires resampling.
• Query framing (consistent/neutral/inconsistent) significantly impacts baseline and RAG performance, so results must be stratified rather than averaged.
• Adversarial context types (e.g., FSAP-IntraQ vs. Rewriter) vary drastically in effectiveness, requiring careful grouping in analysis.

Evidence (verbatim from paper)

Tables 2–5 report ground-truth alignment rates across all closed- and open-source models, including the mean, standard deviation (SD), and 95% confidence intervals (CI) obtained via bootstrapping.

Citation

@misc{amirshahi2025ragrobustness,
  title={Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain},
  author={Amirshahi et al. (2025)},
  year={2025},
  note={arXiv:2509.03787}
}

• arXiv: 2509.03787