Potential and Risks of Large Language Models as Evaluators of Unstructured Text Data

Large Language Models (LLMs) have revolutionized the processing and summarization of unstructured text data. They offer the ability to efficiently analyze complex datasets, such as survey responses, and extract key themes and sentiments. However, with the increasing use of these powerful AI systems to interpret text feedback, a crucial question arises: Can we trust LLMs to accurately reflect the perspectives contained within these text-based datasets?

LLMs excel at generating human-like summaries. At the same time, there is a risk that their output might deviate from the actual content of the original responses. Discrepancies between the output generated by LLMs and the themes actually present in the data could lead to flawed decisions with far-reaching consequences for businesses.

LLMs as Evaluation Instances

Current research investigates the effectiveness of LLMs as evaluation models to assess the thematic consistency of summaries generated by other LLMs. In one study, an Anthropic Claude model was used to create thematic summaries from open-ended survey responses. Amazon Titan Express, Nova Pro, and Meta's Llama served as LLM evaluation instances. This "LLM-as-evaluator" approach was compared to human evaluations, using Cohen's Kappa, Spearman's Rho, and Krippendorff's Alpha for validation. The goal was to find a scalable alternative to traditional, human-centric evaluation methods.

Results and Implications

The results show that while LLMs as evaluators offer a scalable solution comparable to human evaluators, humans may still be better at recognizing subtle, context-specific nuances. This underscores the need for careful consideration when generalizing LLM evaluation models across different contexts and use cases.

The research contributes to the growing body of knowledge on AI-powered text analysis. However, it also highlights the need for further research, particularly regarding addressing biases, improving prompt design, and developing multidisciplinary frameworks to enhance the reliability and fairness of LLM-driven content evaluations.

Mindverse: Your AI Partner

Mindverse, a German all-in-one content tool for AI text, content, images, and research, offers companies the opportunity to effectively leverage the potential of LLMs. In addition to providing a comprehensive platform, Mindverse also develops customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems to support companies in integrating AI into their workflows.

The ongoing research in the field of LLMs and their application as evaluation instances is promising. At the same time, it is crucial to understand the limitations and risks of this technology and to use it responsibly. Mindverse supports companies in leveraging the opportunities of AI while simultaneously addressing the associated challenges.

Bibliography

• Bedemariam, R., Perez, N., Bhaduri, S., Kapoor, S., Gil, A., Conjar, E., Itoku, I., Theil, D., Chadha, A., & Nayyar, N. (2025). Potential and Perils of Large Language Models as Judges of Unstructured Textual Data. arXiv preprint arXiv:2501.08167.
• Tan, Z., Li, D., Wang, S., Beigi, A., Jiang, B., Bhattacharjee, A., Karami, M., Li, J., Cheng, L., & Liu, H. (2024). Large Language Models for Data Annotation and Synthesis: A Survey. arXiv preprint arXiv:2402.13446v3.
• Quelle, D., & Bovet, A. (2024). The perils and promises of fact-checking with large language models. Frontiers in Artificial Intelligence, 7, 1341697.
• Alcaraz, A. (2024). The Promise and Perils of AI-Powered Legal Research. LinkedIn.
• Hugging Face Papers. https://huggingface.co/papers
• The perils and promises of fact-checking with large language models. https://www.researchgate.net/publication/378393759_The_perils_and_promises_of_fact-checking_with_large_language_models
• Exploring the Potential and Perils of AI Writing Support in Scientific Peer Review. https://www.researchgate.net/publication/380181268_MetaWriter_Exploring_the_Potential_and_Perils_of_AI_Writing_Support_in_Scientific_Peer_Review
• ChatPaper. https://www.chatpaper.com/chatpaper/fr?id=3&date=1736870400&page=1
• The influence of social media on lifestyle, body image and eating habits in adolescents. https://pubmed.ncbi.nlm.nih.gov/39015547/