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    <abstract>Analyses of parasocial cues in live-stream chats require accurate, efficient, and scalable annotation. However, manual annotation is tedious, and large language models (LLMs) often make mistakes when applying subjective, discourse-dependent labels. This study proposes Context-aware Batching for Stream Annotation with LLMs (CaBSALLM), an efficient pipeline that incorporates lightweight conversational context and a novel dynamic batching method to improve throughput and scalability. Compared with state-of-the-art pipelines, this generalizable approach is significantly more time- and cost-efficient while achieving comparable or better predictive performance and agreement.</abstract>
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%0 Conference Proceedings
%T CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models
%A Abolhasani, Mohammadsadegh
%A Mousavi, Reza
%A Hu, Paul Jen-Hwa
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F abolhasani-etal-2026-cabsallm
%X Analyses of parasocial cues in live-stream chats require accurate, efficient, and scalable annotation. However, manual annotation is tedious, and large language models (LLMs) often make mistakes when applying subjective, discourse-dependent labels. This study proposes Context-aware Batching for Stream Annotation with LLMs (CaBSALLM), an efficient pipeline that incorporates lightweight conversational context and a novel dynamic batching method to improve throughput and scalability. Compared with state-of-the-art pipelines, this generalizable approach is significantly more time- and cost-efficient while achieving comparable or better predictive performance and agreement.
%U https://aclanthology.org/2026.acl-short.51/
%P 615-636

Markdown (Informal)

[CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models](https://aclanthology.org/2026.acl-short.51/) (Abolhasani et al., ACL 2026)

• CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models (Abolhasani et al., ACL 2026)

ACL

• Mohammadsadegh Abolhasani, Reza Mousavi, and Paul Jen-Hwa Hu. 2026. CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 615–636, San Diego, California, United States. Association for Computational Linguistics.