arXiv AI
Jul 17
Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
arXiv:2607. 14769v1 Announce Type: cross Abstract: Existing text summarization research has focused much on monologic information (e.
DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset
arXiv:2606. 00012v1 Announce Type: cross Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations.
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arXiv AI
Jul 3
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
arXiv:2607. 02504v1 Announce Type: cross Abstract: Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \textbf{speaker recognition}, the task of accurately attributing each spoken utterance to its respective character.
arXiv Machine Learning
Jul 28
IndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages
arXiv:2607. 23242v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed conversational AI, yet high-quality multilingual code-mixed dialogue resources remain scarce, particularly for Indic languages where speakers naturally alternate between English and their native language in both native-script and Romanized forms.
arXiv AI
Jul 28
StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting
arXiv:2607. 24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling.
arXiv Machine Learning
Jun 19
The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
arXiv:2606. 20400v1 Announce Type: new Abstract: Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings.
arXiv AI
Jun 29
HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering
arXiv:2603. 18558v2 Announce Type: replace-cross Abstract: Long-form video question answering requires reasoning over extended temporal contexts, making frame selection a critical bottleneck for multi-modal large language models (MLLMs) bound by finite context windows.
arXiv AI
Jun 3
TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
arXiv:2509. 09685v5 Announce Type: replace-cross Abstract: We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline.
Hugging Face Trending Papers
Jun 22
PIVOTSBench: Evaluating Fine-Grained Interpersonal Relationship Reasoning in Multimodal Large Language Models
Humans possess an innate ability to understand fine-grained interpersonal relationships, which is central to everyday social interactions. Although such reasoning is inherently multimodal, it remains largely unexplored by existing multimodal large language models (MLLMs).
arXiv AI
Jun 15
Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding
arXiv:2605. 21182v2 Announce Type: replace-cross Abstract: Manga is a culturally distinctive multimodal medium and one of the most influential forms of Japanese popular culture.
arXiv AI
Jun 9
Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
arXiv:2606. 07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues.
arXiv AI
Jul 21
Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances
arXiv:2405. 12775v2 Announce Type: replace-cross Abstract: Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions.
arXiv AI
Aug 11
MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks
arXiv:2507. 19634v4 Announce Type: replace-cross Abstract: Recent advances in large language models have laid the foundation for multimodal LLMs (MLLMs), which unify text, speech, and vision within a single framework.