Simulating Meaning, Nevermore! Introducing ICR: A Semiotic-Hermeneutic Metric for Evaluating Meaning in LLM Text Summaries
arXiv:2603.04413v1 Announce Type: new Abstract: Meaning in human language is relational, context dependent, and emergent, arising from dynamic systems of signs rather than fixed word-concept mappings. In computational settings, this semiotic and interpretive complexity complicates the generation and evaluation of meaning. This article proposes an interdisciplinary framework for studying meaning in large language model (LLM) generated language by integrating semiotics and hermeneutics with qualitative research methods. We review prior scholarship on meaning and machines, examining how linguistic signs are transformed into vectorized representations in static and contextualized embedding models, and identify gaps between statistical approximation and human interpretive meaning. We then introduce the Inductive Conceptual Rating (ICR) metric, a qualitative evaluation approach grounded in inductive content analysis and reflexive thematic analysis, designed to assess semantic accuracy and m
arXiv:2603.04413v1 Announce Type: new Abstract: Meaning in human language is relational, context dependent, and emergent, arising from dynamic systems of signs rather than fixed word-concept mappings. In computational settings, this semiotic and interpretive complexity complicates the generation and evaluation of meaning. This article proposes an interdisciplinary framework for studying meaning in large language model (LLM) generated language by integrating semiotics and hermeneutics with qualitative research methods. We review prior scholarship on meaning and machines, examining how linguistic signs are transformed into vectorized representations in static and contextualized embedding models, and identify gaps between statistical approximation and human interpretive meaning. We then introduce the Inductive Conceptual Rating (ICR) metric, a qualitative evaluation approach grounded in inductive content analysis and reflexive thematic analysis, designed to assess semantic accuracy and meaning alignment in LLM-outputs beyond lexical similarity metrics. We apply ICR in an empirical comparison of LLM generated and human generated thematic summaries across five datasets (N = 50 to 800). While LLMs achieve high linguistic similarity, they underperform on semantic accuracy, particularly in capturing contextually grounded meanings. Performance improves with larger datasets but remains variable across models, potentially reflecting differences in the frequency and coherence of recurring concepts and meanings. We conclude by arguing for evaluation frameworks that leverage systematic qualitative interpretation practices when assessing meaning in LLM-generated outputs from reference texts.
Executive Summary
This article proposes a novel framework, Inductive Conceptual Rating (ICR), to evaluate meaning in large language model (LLM) generated text summaries. By integrating semiotics, hermeneutics, and qualitative research methods, ICR assesses semantic accuracy and meaning alignment beyond lexical similarity metrics. An empirical comparison across five datasets reveals that LLMs underperform in capturing contextually grounded meanings. The study highlights the importance of leveraging systematic qualitative interpretation practices for evaluating LLM-generated outputs. The ICR metric offers a promising approach to addressing the limitations of existing metrics, which prioritize linguistic similarity over semantic accuracy.
Key Points
- ▸ The article introduces ICR, a semiotic-hermeneutic metric for evaluating meaning in LLM text summaries.
- ▸ ICR integrates semiotics, hermeneutics, and qualitative research methods to assess semantic accuracy and meaning alignment.
- ▸ The study reveals that LLMs underperform in capturing contextually grounded meanings, particularly in smaller datasets.
Merits
Strength in Addressing Limitations
The article identifies and addresses the limitations of existing metrics, which prioritize linguistic similarity over semantic accuracy.
Novel Approach to Evaluating LLM Outputs
ICR offers a unique framework for evaluating meaning in LLM-generated text summaries, moving beyond lexical similarity metrics.
Empirical Comparison with Human-Generated Summaries
The study provides an empirical comparison across five datasets, highlighting the importance of systematic qualitative interpretation practices.
Demerits
Limited Generalizability
The study's findings may not generalize to other domains or applications, requiring further research to validate the ICR metric.
Potential Computational Challenges
The implementation of ICR may require significant computational resources, potentially limiting its adoption in resource-constrained settings.
Expert Commentary
This article makes a significant contribution to the field of natural language processing by introducing a novel framework for evaluating meaning in LLM-generated text summaries. The ICR metric offers a promising approach to addressing the limitations of existing metrics, which prioritize linguistic similarity over semantic accuracy. However, further research is needed to validate the ICR metric and explore its potential applications. The study's findings also highlight the importance of human-AI collaboration in evaluating LLM-generated outputs, emphasizing the need for systematic qualitative interpretation practices. As LLMs continue to play an increasingly important role in information production and dissemination, the development of effective evaluation metrics like ICR is essential for ensuring the quality and accuracy of LLM-generated content.
Recommendations
- ✓ Future research should focus on validating the ICR metric across diverse domains and applications.
- ✓ Developers should prioritize the integration of ICR or similar metrics into LLM pipelines to improve the quality and accuracy of generated content.