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        <title>Semantic Variability of LLM-Generated Replies Across LLMs: Implications for Designing Conversation-Based Assessment</title>
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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.</abstract>
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%0 Conference Proceedings
%T Semantic Variability of LLM-Generated Replies Across LLMs: Implications for Designing Conversation-Based Assessment
%A Hao, Jiangang
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-2-4
%F hao-2026-semantic
%X 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.
%U https://aclanthology.org/2026.aimecon-sessions.10/
%P 82-88

Markdown (Informal)

[Semantic Variability of LLM-Generated Replies Across LLMs: Implications for Designing Conversation-Based Assessment](https://aclanthology.org/2026.aimecon-sessions.10/) (Hao, AIME-Con 2026)

• Semantic Variability of LLM-Generated Replies Across LLMs: Implications for Designing Conversation-Based Assessment (Hao, AIME-Con 2026)

ACL

• Jiangang Hao. 2026. Semantic Variability of LLM-Generated Replies Across LLMs: Implications for Designing Conversation-Based Assessment. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers, pages 82–88, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).