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Researchers from KTH Royal Institute of Technology propose a two-stage framework that fine-tunes LLMs on dialogue transcripts and uses contrastive learning to create joint embeddings for aligning backchannel signals with conversational context, demonstrating improved context-backchannel retrieval compared to previous methods.