AI Research Analysis
Evaluating the Impact of LLM-Assisted Annotation in a Perspectivized Setting: the Case of FrameNet Annotation
By Frederico Belcavello, Ely Matos, Arthur Lorenzi, Lisandra Bonoto, Lívia Ruiz, Luiz Fernando Pereira, Victor Herbst, Yulla Navarro, Helen de Andrade Abreu, Lívia Dutra, Tiago Timponi Torrent
Abstract: The use of LLM-based applications as a means to accelerate and/or substitute human labor in the creation of language resources and dataset is a reality. Nonetheless, despite the potential of such tools for linguistic research, comprehensive evaluation of their performance and impact on the creation of annotated datasets, especially under a perspectivized approach to NLP, is still missing. This paper contributes to reduction of this gap by reporting on an extensive evaluation of the (semi-)automatization of FrameNet-like semantic annotation by the use of an LLM-based semantic role labeler. The methodology employed compares annotation time, coverage and diversity in three experimental settings: manual, automatic and semi-automatic annotation. Results show that the hybrid, semi-automatic annotation setting leads to increased frame diversity and similar annotation coverage, when compared to the human-only setting, while the automatic setting performs considerably worse in all metrics, except for annotation time.
Executive Impact: Hybrid LLM Approach Boosts Frame Diversity
This research reveals that LLM-assisted annotation, when integrated into FrameNet workflows, significantly enhances the diversity of semantic frame interpretations without compromising quality. This offers a scalable and linguistically robust path for expanding complex language resources.
80.91
Avg Unique Frames / Doc (Hybrid)
90.65%
Min Core FEs (Hybrid)
12.97min
Avg Annotation Time / Sentence (Hybrid)
65.45%
LLM Annotations Improved by Humans
Deep Analysis & Enterprise Applications
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Semi-Automatic Annotation
Description: Integration of LLM-generated suggestions into human annotation workflows for validation, correction, refinement, or deletion.
Explanation: This hybrid approach aims to combine LLM scalability with human linguistic depth, focusing on preserving interpretive nuances inherent in FrameNet. It allows annotators to work from a machine-provided baseline, enhancing efficiency where possible without sacrificing quality.
Perspectivized Annotation
Description: FrameNet's approach acknowledges that meaning is interpretive, allowing for multiple plausible frames depending on context, and recognizing legitimate differences in interpretation.
Explanation: Unlike categorical semantic role labeling, FrameNet emphasizes the viewpoint and conceptual stance encoded in frames. LLM assistance must be evaluated to ensure it supports, rather than distorts, these perspectival distinctions, which are crucial for the model's epistemological strength.
Frame Diversity
Description: Measures the number of unique frames associated with each document and the average per sentence across different annotation settings.
Explanation: This metric assesses whether LLMs interfere with human judgment regarding frame interpretations. A higher number of unique frames suggests more perspectives are captured, which aligns with FrameNet's goals. The study found the hybrid approach increased diversity compared to human-only.
Annotation Coverage & Core FEs
Description: Evaluates the total number of annotated elements (documents, sentences, ASs, FEs) and the percentage of minimal core Frame Elements (FEs) present.
Explanation: Coverage indicates the breadth of annotation. The percentage of minimal core FEs assesses adherence to FrameNet's methodological requirement for frame instantiation. LOME (automatic) performed poorly on core FEs due to not handling null instantiations, but the hybrid approach maintained human-level quality.
Annotation Speed Impact
No Significant Speed ImprovementLLM pre-annotation did not statistically significantly reduce human annotation time. This suggests that the primary benefit is not speed but enhanced quality and diversity.
Enterprise Process Flow
Machine Transcription
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LLM Pre-Annotation (LOME)
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Human Review & Correction
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Final Annotated Dataset
| Metric | Human-Only | LLM-Assisted (Hybrid) | Fully Automatic |
|---|---|---|---|
| Avg Unique Frames / Doc | 67.91 | 80.91 | 52.66 |
| Avg ASs / Doc | 129 | 160 | 126 |
| Min Core FEs % | 95.79% | 90.65% | 34.20% |
| Avg Annotation Time (min/sentence) | 14.96 | 12.97 | N/A (Very Fast) |
Impact on Annotation Quality and Judgment
The study found that while LLM pre-annotation didn't accelerate the process significantly, it did not negatively impact human judgment or the quality of the final annotations. Annotators largely preserved human judgments and improved machine suggestions, leading to a high-quality dataset.
- LLM-assisted approach preserves human judgment: The hybrid method successfully leveraged LLMs to improve coverage and diversity while ensuring human experts could validate and refine annotations, maintaining high quality.
- LLM suggestions are a valuable starting point: A significant portion (65.45%) of LOME's automatic annotations were partially used and improved by annotators, demonstrating their utility as a foundational layer for human refinement.
- Rigorous human oversight remains crucial: The need for expert validation in LLM-assisted settings is highlighted to prevent biases and errors, reinforcing the value of the hybrid model over fully automatic systems.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings by integrating AI-assisted processes into your enterprise's language resource annotation workflows.
Estimated Annual Savings
$0
Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach ensures successful integration of LLM-assisted tools into your annotation pipeline.
Phase 01: Strategy & Setup
Initial consultation to define specific annotation needs, integrate LOME or similar LLM-based parsers, and establish custom guidelines for perspectivized annotation.
Phase 02: Pilot & Refinement
Run a pilot with a subset of your data. Gather feedback from expert annotators on LLM suggestions, refine prompts, and fine-tune the human-in-the-loop workflow to optimize diversity and quality metrics.
Phase 03: Full-Scale Deployment & Monitoring
Integrate the refined LLM-assisted annotation system across your team. Implement continuous monitoring of annotation quality, diversity, and efficiency, with iterative improvements based on performance data.
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