Challenge: Existing multimodal large language models are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations.
Approach: They propose a large-scale multi-turn multimodal dialogue dataset that uses rules and GPT assistance to generate a multi-turned multimodal dialog dataset.
Outcome: The proposed dataset is a strong benchmark for multi-turn multimodal dialogue learning . it features complex dialogues with contextual dependencies that force models to track, ground, and recall information across multiple turns and disparate visual regions.

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Challenge: Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
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CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships? (2025.acl-long)

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Challenge: Existing benchmarks focus on assessing factual and logical correctness in downstream tasks with limited emphasis on evaluating MLLMs’ ability to interpret pragmatic cues and intermodal relationships.
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Grounding Multilingual Multimodal LLMs With Cultural Knowledge (2025.emnlp-main)

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MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues (2024.acl-long)

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Challenge: Large Language Models (LLMs) have greatly enhanced dialogue systems, but evaluation of their capabilities remains a challenge.
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Challenge: Existing conversational QA systems only use a single knowledge source, e.g., paragraphs or knowledge graph, and assume it contains enough evidence to extract answers to users' questions.
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Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models (2026.acl-long)

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Challenge: Steering methods have emerged as effective tools for guiding large language models’ behavior, yet multimodal large language model lacks comparable techniques due to architectural diversity and limited availability of multimodal steering vectors.
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