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Semantic Variability of Replies Across LLMs: Implications for Designing Conversation-Based Assessment

A study submitted to arXiv on 13 Aug 2026 found that LLM-generated replies vary semantically when the underlying model changes, with both model choice and conversational context affecting response similarity and alignment with human replies. The findings suggest that prompting and context alone may not ensure consistent responses across LLMs, underscoring the need for infrastructure and design strategies to maintain stability as models evolve.

read1 min views1 publishedAug 27, 2026
Semantic Variability of Replies Across LLMs: Implications for Designing Conversation-Based Assessment
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[Submitted on 13 Aug 2026]


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Abstract:This study examines whether LLM-generated replies remain semantically consistent when the underlying LLM changes. Using messages from real collaborative conversations, we compared the semantic similarity of generated replies across LLMs under two conditions: with and without preceding chat history. Results show that model choice and conversational context both affect response similarity and alignment with human replies. These findings indicate that prompting and conversational context alone may not be sufficient to preserve response consistency across LLMs, highlighting the need for infrastructure and design strategies that can maintain stable and comparable responses amid the rapid and continuous evolution of LLMs.

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