# Using Human-LLM Disagreement to Improve Checklist-Based Quality Appraisal

arXiv: https://arxiv.org/abs/2608.20385 · HTML: https://mlsanity.com/paper/2608.20385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering](https://mlsanity.com/paper/2404.04302/) (2024)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages](https://mlsanity.com/paper/2310.17526/) (2023)
- [How Susceptible are LLMs to Influence in Prompts?](https://mlsanity.com/paper/2408.11865/) (2024)
- [Automated Paper Screening for Clinical Reviews Using Large Language Models](https://mlsanity.com/paper/2305.00844/) (2023)
- [RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering](https://mlsanity.com/paper/2407.15621/) (2024)
- [What Level of Automation is "Good Enough"? A Benchmark of Large Language Models for Meta-Analysis Data Extraction](https://mlsanity.com/paper/2507.15152/) (2025)

## Abstract (arXiv)

Systematic reviews rely on quality appraisal of included studies, a process that is time-consuming and sensitive to ambiguity in checklist criteria. Although large language models (LLMs) offer opportunities to support these tasks, appraisal checklists are typically treated as fixed inputs, and it remains unclear how their design affects agreement with expert judgments. Therefore, we investigate (1) whether LLMs can approximate human judgments in checklist-based appraisal and (2) whether patterns of human-LLM disagreement can be used to identify and improve ambiguous checklist items. Using the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist, we compare LLM-generated assessments with expert annotations across three research topics and two checklist versions. Agreement is assessed using item-level accuracy, chance-corrected agreement, and preservation of study-level rank ordering. We find that performance varies substantially across checklist items, with ambiguous and conditional criteria producing the greatest disagreement. Revising these items improves both raw and chance-corrected agreement. Although item-level misclassifications persist, LLM-generated scores often preserve the relative ranking of studies when high-agreement items are retained. These results indicate that reliable LLM-assisted appraisal depends not only on model choice but also on checklist design. The findings suggest that analyzing human-LLM disagreement can help identify problematic checklist items and support the iterative improvement of research synthesis workflows.