# Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios

arXiv: https://arxiv.org/abs/2501.11269 · HTML: https://mlsanity.com/paper/2501.11269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
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- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
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- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback](https://mlsanity.com/paper/2406.01771/) (2024)
- [A Primer on Pretrained Multilingual Language Models](https://mlsanity.com/paper/2107.00676/) (2021)
- [XPersona: Evaluating Multilingual Personalized Chatbot](https://mlsanity.com/paper/2003.07568/) (2020)
- [Zero-shot cross-lingual transfer language selection using linguistic similarity](https://mlsanity.com/paper/2301.13720/) (2023)
- [GSN: A Graph-Structured Network for Multi-Party Dialogues](https://mlsanity.com/paper/1905.13637/) (2019)
- [PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment](https://mlsanity.com/paper/2407.16222/) (2024)
- [Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noise](https://mlsanity.com/paper/2109.06772/) (2021)
- [Polyglot Contextual Representations Improve Crosslingual Transfer](https://mlsanity.com/paper/1902.09697/) (2019)
- [SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats](https://mlsanity.com/paper/1909.07734/) (2019)

## Abstract (arXiv)

Current multilingual large language models(MLLMs) still focus on simple question-answering formats, often overlooking more complex dialogue scenarios. In other words, their capabilities of multilingual large models have yet to be validated in dialogue tasks with intricate structures. We therefore ask, Q1: How well do LLMs generalize to more complex dialog scenarios? Q2: Can supervised fine-tuning on a high-quality parallel benchmark restore this ability? Q3: Does the "multilingual complementarity" effect survive in the setting? To answer these questions, we introduce XMP, a high-quality parallel Multilingual dataset sourced from Multi-party Podcast dialogues, which is the first parallel dataset focusing on multi-party dialogue scenarios. Most samples in the dataset feature three or more participants, discussing a wide range of topics. Through extensive experiments, we find that, R1: MLLMs fail to generalize to multi-party setting, R2 Fine-tuning on XMP improves only marginally, with the 70B model achieving at most a 1% absolute gain over its 8B counterpart; R3: Mixing languages during SFT is usually detrimental, with any benefits being marginal and limited to isolated cases in the 70B model.