# Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs

arXiv: https://arxiv.org/abs/2503.08842 · HTML: https://mlsanity.com/paper/2503.08842/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners](https://mlsanity.com/paper/2205.10747/) (2022)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [A Survey of Large Language Models Attribution](https://mlsanity.com/paper/2311.03731/) (2023)
- [EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models](https://mlsanity.com/paper/2502.04424/) (2025)
- [Improved Deep Learning Baselines for Ubuntu Corpus Dialogs](https://mlsanity.com/paper/1510.03753/) (2015)
- [SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Script](https://mlsanity.com/paper/2410.20682/) (2024)
- [Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation](https://mlsanity.com/paper/2412.05342/) (2024)

## Built on by (most cited first, 2 of 2)

- [FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management](https://mlsanity.com/paper/2505.15347/) (2025)
- [MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation](https://mlsanity.com/paper/2510.05124/) (2025)

## Abstract (arXiv)

Multi-party dialogue generation presents significant challenges due to the complex interplay of multiple speakers and interwoven conversational threads. Traditional approaches often fall short in capturing these complexities, particularly when relying on manually annotated dialogue relations. This paper introduces Speaker-Attentive LLM (SA-LLM), a novel generative model that leverages pre-trained Large Language Models (LLMs) and a speaker-aware contrastive learning strategy to address these challenges. SA-LLM incorporates a speaker-attributed input encoding and a contrastive learning objective to implicitly learn contextual coherence and speaker roles without explicit relation annotations. Extensive experiments on the Ubuntu IRC and Movie Dialogues datasets demonstrate that SA-LLM significantly outperforms state-of-the-art baselines in automatic and human evaluations, achieving superior performance in fluency, coherence, informativeness, and response diversity. Ablation studies and detailed error analyses further validate the effectiveness of the proposed speaker-attentive training approach, highlighting its robustness across different speaker roles and context lengths. The results underscore the potential of SA-LLM as a powerful and annotation-free solution for high-quality multi-party dialogue generation.