ArticleScientific reports2025
LLMs outperform outsourced human coders on complex textual analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Walkable Intelligent Parks: Can a Large Language Model Turn Urban Park Audit Findings into Actionable Recommendations?International journal of environmental research and public health · 2026Article
- Collective problem decomposition improves the wisdom of deliberative crowds.Nature communications · 2026Article
- Scaling Open-Ended Survey Responses Using LLM-Paired Comparisons.Public opinion quarterly · 2026Article
- Four decades of ADHD: a systematic AI-assisted analysis of conceptual shifts across six DSM editions.Frontiers in psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper evaluates the effectiveness of large language models (LLMs) in extracting complex information from text data. Using a corpus of Spanish news articles, we compare how accurately various LLMs and outsourced human coders reproduce expert annotations on five natural language processing tasks, ranging from named entity recognition to identifying nuanced political criticism in news articles. We find that LLMs consistently outperform outsourced human coders, particularly in tasks requiring deep contextual understanding. These findings suggest that current LLM technology offers researchers without programming expertise a cost-effective alternative for sophisticated text analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.