title: Potential and Perils of Large Language Models as Judges of Unstructured
    Textual Data
summary: Rapid advancements in large language models have unlocked remarkable
  capabilities when it comes to processing and summarizing unstructured text
  data. This has implications for the analysis of rich, open-ended datasets, such
  as survey responses, where LLMs hold the promise of efficiently distilling key
  themes and sentiments. However, as organizations increasingly turn to these
  powerful AI systems to make sense of textual feedback, a critical question
  arises, can we trust LLMs to accurately represent the perspectives contained
  within these text based datasets? While LLMs excel at generating human-like
  summaries, there is a risk that their outputs may inadvertently diverge from
  the true substance of the original responses. Discrepancies between the
  LLM-generated outputs and the actual themes present in the data could lead to
  flawed decision-making, with far-reaching consequences for organizations. This
  research investigates the effectiveness of LLMs as judge models to evaluate the
  thematic alignment of summaries generated by other LLMs. We utilized an
  Anthropic Claude model to generate thematic summaries from open-ended survey
  responses, with Amazon's Titan Express, Nova Pro, and Meta's Llama serving as
  LLM judges. The LLM-as-judge approach was compared to human evaluations using
  Cohen's kappa, Spearman's rho, and Krippendorff's alpha, validating a scalable
  alternative to traditional human centric evaluation methods. Our findings
  reveal that while LLMs as judges offer a scalable solution comparable to human
  raters, humans may still excel at detecting subtle, context-specific nuances.
  This research contributes to the growing body of knowledge on AI assisted text
  analysis. We discuss limitations and provide recommendations for future
  research, emphasizing the need for careful consideration when generalizing LLM
  judge models across various contexts and use cases.
paper_authors._id: 678748ee95b9364769858656
paper_authors.name: Rewina Bedemariam
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paper_authors.fullname: Rewina Bedemariam
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paper_authors.name: Natalie Perez
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paper_authors.name: Sreyoshi Bhaduri
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paper_authors.fullname: sreyoshi bhaduri
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paper_authors.name: Satya Kapoor
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paper_authors.name: Alex Gil
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paper_authors.fullname: Alex Gil
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paper_authors.name: Elizabeth Conjar
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paper_authors.name: Ikkei Itoku
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paper_authors.fullname: Ikkei Itoku
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paper_authors.user: ikkeiitoku
paper_authors._id: 678748ee95b936476985865d
paper_authors.name: David Theil
paper_authors._id: 678748ee95b936476985865e
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
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paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 678748ee95b936476985865f
paper_authors.name: Naumaan Nayyar
paper_title: Potential and Perils of Large Language Models as Judges of Unstructured
    Textual Data
paper_summary: Rapid advancements in large language models have unlocked remarkable
  capabilities when it comes to processing and summarizing unstructured text
  data. This has implications for the analysis of rich, open-ended datasets, such
  as survey responses, where LLMs hold the promise of efficiently distilling key
  themes and sentiments. However, as organizations increasingly turn to these
  powerful AI systems to make sense of textual feedback, a critical question
  arises, can we trust LLMs to accurately represent the perspectives contained
  within these text based datasets? While LLMs excel at generating human-like
  summaries, there is a risk that their outputs may inadvertently diverge from
  the true substance of the original responses. Discrepancies between the
  LLM-generated outputs and the actual themes present in the data could lead to
  flawed decision-making, with far-reaching consequences for organizations. This
  research investigates the effectiveness of LLMs as judge models to evaluate the
  thematic alignment of summaries generated by other LLMs. We utilized an
  Anthropic Claude model to generate thematic summaries from open-ended survey
  responses, with Amazon's Titan Express, Nova Pro, and Meta's Llama serving as
  LLM judges. The LLM-as-judge approach was compared to human evaluations using
  Cohen's kappa, Spearman's rho, and Krippendorff's alpha, validating a scalable
  alternative to traditional human centric evaluation methods. Our findings
  reveal that while LLMs as judges offer a scalable solution comparable to human
  raters, humans may still excel at detecting subtle, context-specific nuances.
  This research contributes to the growing body of knowledge on AI assisted text
  analysis. We discuss limitations and provide recommendations for future
  research, emphasizing the need for careful consideration when generalizing LLM
  judge models across various contexts and use cases.
paper_ai_keywords: LLMs (Large Language Models) sequential capabilities processing summarizing unstructured text data rich, open-ended datasets survey responses key themes and sentiments finding classification trust thematic alignment summaries open-ended survey responses human-like summaries LLM-as-judges judge models thematic summaries AnthropicSM model Amazon's Titan Express Nova Pro Meta's Llama student models Cohen's kappa Spearman's rho Krippendorff's alpha scalable alternative traditional human-centric evaluation methods subtle, context-specific nuances AI-assisted text analysis