MLLM能否泛化到多方对话？探索复杂场景下的多语言回复生成
Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios
AI总结：
针对多语言大模型在复杂多方对话场景泛化性未被验证的问题，研究者构建了首个多方对话平行多语言数据集XMP，实验发现模型泛化性差、微调提升有限、混合语言微调通常有害。
摘要：
当前多语言大语言模型（MLLM）仍聚焦于简单的问答形式，往往忽视更复杂的对话场景。换言之，多语言大模型的能力尚未在结构复杂的对话任务中得到验证。因此我们提出三个问题：Q1：大语言模型（LLM）对更复杂对话场景的泛化能力如何？Q2：在高质量平行基准上进行监督微调能否恢复该能力？Q3：“多语言互补”效应在该设定下是否仍然存在？为解答这些问题，我们推出XMP——一个源自多方播客对话的高质量平行多语言数据集，这是首个聚焦多方对话场景的平行数据集。数据集中大多数样本包含三名及以上参与者，讨论话题广泛。通过大量实验我们发现：R1：MLLM无法泛化到多方对话设定；R2：在XMP上微调仅带来边际提升，70B模型较8B模型的绝对增益最高仅为1%；R3：监督微调（SFT）期间混合语言通常有害，任何益处都微乎其微，且仅限于70B模型的个别情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
