Multi-Modal Dialogue Generation

• Multi-modal dialogue generation is the design of conversational models that integrate text, images, video, and audio to produce contextually grounded responses.
• Modern approaches employ cross-modal transformer fusion and unified pre-training to merge heterogeneous inputs for coherent and natural dialogue.
• Research emphasizes robust dataset curation, multi-stage training, and comprehensive evaluation metrics to ensure consistency and real-world applicability.

Multi-modal dialogue generation is a research area focused on designing models that produce conversational responses conditioned on heterogeneous inputs, typically spanning text, images, video, and audio. The objective is to enable dialogue agents that both comprehend and generate responses reflecting the information embedded in various modalities, resulting in more natural, contextually grounded, and informative interactions. Modern approaches integrate advances in large pre-trained LLMs, vision transformers, and joint cross-modal fusion architectures, often coupled with domain knowledge or dynamic context models. This article delineates the task formalization, dataset methodologies, representative architectures, training and evaluation protocols, experimental findings, and open challenges in multi-modal dialogue generation.

1. Task Formalization and Objectives

The canonical multi-modal dialogue generation task is defined over datasets $\mathcal{D}=\{(c_i, M_i, r_i)\}_n$, where $c_i$ is the textual dialogue context (previous $k$ utterances), $M_i$ is an optional set of associated images, video, or audio clips, and $r_i$ is the human response (text). The underlying objective is typically the maximization of log-likelihood for reference responses conditioned on all available modalities:

$$L(\theta) = -\sum_{i=1}^{n} \log\, p_\theta(r_i \mid c_i, M_i)$$

For open-domain and task-oriented dialogues, the context may further incorporate external knowledge bases, GUI states, or session/scene labels. In video/audio-grounded scenarios, segments are extracted and treated as additional input tokens to the model (Moskvoretskii et al., 2023, Li et al., 2020, Pang et al., 2 Dec 2025). For dialogue generation beyond text (e.g., image or video reply), the conditional policy generalizes to $$p(r_t^{\text{Text}}, r_t^{\text{Image}}\,|\,D_{1:t-1})$$ (Yoon et al., 2024).

2. Dataset Curation and Benchmarking

Early work often resorted to modest, hand-curated datasets (e.g., MMD—∼100K dialogs in fashion domain with images per turn (Agarwal et al., 2018)), but recent advances emphasize large-scale, automated multi-modal corpora:

• IMAD Dataset Construction: Employs a two-stage process—first, utterance-image matching via CLIP similarity and Sentence-BERT for topical coherence (thresholds $\alpha=0.3$, $\beta=0.8$)—second, candidate pairs are filtered by VQA overlap with noun tokens (≥50%) (Moskvoretskii et al., 2023). Produces 48,732 multi-turn dialogues, with multi-modal splits for robust training and evaluation.
• MMDialog Corpus: 1.08 million real-world social-media multimodal threads, 1.53 million unique images, across 4,184 topics (Feng et al., 2022). Supports both retrieval-based and generative paradigms.
• PhotoChat, DialogCC, and TikTalk: Address object-consistent photo sharing and real-world video chitchat (Guo et al., 2024, Lin et al., 2023).
• DialogBen: A benchmark for multi-modal interactive dialogue systems with 64 modality-switching variants and bilingual coverage; evaluates both modal correctness and generative coherence (Huang et al., 2024).
• Scene-aware and GUI-grounded datasets: Datasets tailored for session/scene transitions (Li et al., 2022) and task-oriented interactions with GUI screenshots and operation sequences (Yang et al., 16 Nov 2025).

3. Model Architectures and Cross-modal Fusion

Modern architectures employ transformer-based encoders and decoders, often with explicit cross-modal fusion:

Cross-modal Transformer Fusion

• Text and image (or video/audio) features are extracted via pre-trained BERT, ViT, or I3D/VGGish.
• Token and patch feature streams are concatenated and passed through several transformer layers with shared multi-head attention over all modalities:

$$A^{\ell} = \text{softmax}\left(\frac{Q^{\ell}K^{\ell T}}{\sqrt{d}}\right)$$

• The decoder, typically a GPT-style transformer, attends via encoder-decoder cross-attention (Moskvoretskii et al., 2023, Li et al., 2020).

End-to-end Perception-to-Generation

• Image content is encoded by ViT and Q-Former, visual tokens are injected into an LLM token stream.
• Image generation is achieved via Stable Diffusion, conditioned on dynamic vocabulary mappings and straight-through Gumbel-Softmax for end-to-end differentiability and gradient flow (Guo et al., 2024).
• Citation modules and cross-attention conditioning facilitate object consistency across sequential images (Yoon et al., 2024).

Scene-Aware, Knowledge-Aware, and Graph Reasoning

• Session/scene prompts compose model inputs with visual captions and fixed template labels; multi-task transformers jointly predict boundaries and responses (Li et al., 2022).
• Models such as MDS-S² and DKMD integrate attribute and relation knowledge via $n$-hop graph walks and dual knowledge selection with graph attention refinement (Chen et al., 2023, Chen et al., 2022).
• Semantic graph RL agents collaboratively walk multimodal graphs and translate reasoning paths into PLM-compatible sequences (Zhao et al., 2022).

Video-Audio Generation

• Architectures decouple understanding (Conductor: reasoning, splitting into speech and motion tokens) from realization (Creator: autoregressive audio and diffusion-based video generation) with explicit fusion layers for cross-modal and temporal coherence (Pang et al., 2 Dec 2025).

4. Training Protocols and Optimization

Models are invariably fine-tuned with AdamW or Adam optimizers, cross-entropy or MSE objectives, with batch sizes and learning rates selected for stability and scale. Key recipes include:

• Cosine or linear learning rate schedules; frequent dropout (0.1–0.5) in transformer blocks (Moskvoretskii et al., 2023, Guo et al., 2024, Pang et al., 2 Dec 2025).
• Detailed joint loss formulations combine generation loss with auxiliary (image-text contrastive, representation regularization, intent prediction) objectives, with explicit balancing weights (Moskvoretskii et al., 2023, Guo et al., 2024, Yoon et al., 2024).
• Multi-stage pre-training is standard: image/text modules trained on large-scale unimodal or paired corpora, followed by joint multimodal fine-tuning on scarce dialogue data (e.g., Divter protocol) (Sun et al., 2021).
• Two-stage training (language-only, then full multimodal) is reported for OpenFlamingo-style architectures (Yoon et al., 2024).
• For pipeline approaches, image captioners, text generators, and image synthesizers are trained independently or sequentially, but end-to-end gradient flow is not maintained (Guo et al., 2024).

5. Evaluation Protocols and Comparative Findings

Evaluation is multi-faceted, blending automatic metrics and human assessment:

Automatic Metrics

• BLEU-1…4, METEOR, ROUGE-L, CIDEr for text generation (Moskvoretskii et al., 2023, Feng et al., 2022, Lin et al., 2023, Pang et al., 2 Dec 2025).
• Inception Score (IS), Fréchet Inception Distance (FID) for generated images (Guo et al., 2024, Yoon et al., 2024, Sun et al., 2021).
• MM-Relevance: joint text-image semantic alignment using CLIP dot products (Feng et al., 2022).
• Modality-switching accuracy and VQA-based coherence for multi-modal pipelines (Huang et al., 2024).

Quantitative Highlights

Model/Metric	BLEU-4	METEOR	ROUGE-L	IS	FID	MM-rel	Acc (%)
IMAD MM	12.3	17.4	26.8	—	—	—	—
Text-only Abl.	10.1	15.2	24.1	—	—	—	—
End-to-End Photo	12.08	—	11.00	14.47	75.88	—	—
Divter/MMDialog	—	—	—	20.53	—	61.85	—
DialogGen	—	—	—	—	—	—	97.2
BI-MDRG MMDialog	—	—	—	22.4	—	—	—

Human evaluation across relevance, informativeness, sensibility, and visual grounding consistently favors models with explicit multi-modal fusion and knowledge integration.

Key qualitative findings are that multi-modal models proactively reference and reason over visual content, grounding responses explicitly in images, video cues, and session context variables (Moskvoretskii et al., 2023, Li et al., 2020, Zhao et al., 2022). Failure cases are typically tied to uninformative image representations or poor filter quality in candidate selection.

6. Representative Experimental Examples

In all cases, end-to-end or fused models are observed to deliver visually and contextually grounded responses. For instance:

• IMAD’s multi-modal model interprets a mountain sunrise by linking the visual input directly to the text response: "That sunrise looks breathtaking! Did you climb up that ridge for the view?" as opposed to generic, image-agnostic replies (Moskvoretskii et al., 2023).
• Universal transformers for video–audio dialogue reference both the optical flow (actions) and audio cues: "Yes. I hear the ball squeaking loudly as he chews on it." (Li et al., 2020).
• Citation-modulated prompts in BI-MDRG ensure sequential images exhibit object consistency across turns (Yoon et al., 2024).

7. Current Challenges and Research Directions

The main open challenges are:

• Visual Grounding: Ensuring robust and fine-grained semantic alignment between generated responses and visual/audio context; improving representations for nuanced reasoning over imagery or dynamic content.
• Scaling: Overcoming quadratic attention/computation bottlenecks when modeling long dialog histories or multi-image/video contexts.
• Multi-modal Consistency: Maintaining object, scene, or session consistency across multi-turn, multi-modal outputs, notably for image or video responses (Yoon et al., 2024).
• Unified Pre-training: Integrating joint pre-training over web-scale image–text–dialog data to boost transfer, compositionality, and flexibility (Moskvoretskii et al., 2023, Pang et al., 2 Dec 2025).
• Knowledge Integration: Leveraging both attribute and relation knowledge from structured or unstructured KBs for factual and contextual grounding (Chen et al., 2023, Chen et al., 2022).
• Evaluation: Designing benchmarks and metrics that reliably assess cross-modal coherence, switching accuracy, and object/image consistency in generated conversations (Huang et al., 2024).
• Real-world Generalization: Facilitating domain-transfer, GUI adaptation, and dynamic context modeling for practical deployment settings (Yang et al., 16 Nov 2025).

Future directions include joint vision–language reasoning architectures, object-centric citation mechanisms, human-in-the-loop curation, and unified diffusion–autoregressive pipelines supporting fully interactive multi-modal dialogue agents.

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References:

(Moskvoretskii et al., 2023, Li et al., 2020, Guo et al., 2024, Agarwal et al., 2018, Li et al., 2022, Chen et al., 2023, Feng et al., 2022, Yoon et al., 2024, Pang et al., 2 Dec 2025, Sun et al., 2021, Lin et al., 2023, Yang et al., 16 Nov 2025, Zhao et al., 2022, Huang et al., 2024, Chen et al., 2022, Firdaus et al., 2023, Wang et al., 31 Jan 2025)