From Capability Signals to Reliability Evidence: A Semi-Systematic Review of Evaluation Tools for Large Language Models
Abstract
This repository contains the supplementary documentation and machine-readable research data supporting a semi-systematic literature review and evidence map of large language model (LLM) reliability research. The review examines the inferential distance between observable capability signals and stronger reliability claims across four analytical areas: 1. reasoning faithfulness;2. factual grounding;3. evaluation validity; and4. evidence use in retrieval-augmented generation. The repository is intended to support reviewer inspection, study-level claim tracing, bibliographic verification, and methodological audit. It does not contain copyrighted full texts of the reviewed publications.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.