CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models (2026.acl-short)
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| Challenge: | Large-scale annotations of subjective, discourse-dependent social interactions remain a critical bottleneck in computational social science. |
| Approach: | They propose a pipeline that incorporates lightweight conversational context and a dynamic batching method to improve throughput and scalability. |
| Outcome: | The proposed pipeline improves throughput and scalability while preserving interpretive depth essential to complex social annotations. |
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