Search Results for author:
Found 17 papers, 7 papers with code
Fundamental Exploration of Evaluation Metrics for Persona Characteristics of Text Utterances
When evaluating the appropriateness of a large number of arbitrary utterances to be registered in the utterance database of a retrieval-based dialog system, evaluation metrics that require a reference (or a “correct” utterance) for each evaluation target cannot be used.
CARE: Aligning Language Models for Regional Cultural Awareness
Existing language models (LMs) often exhibit a Western-centric bias and struggle to represent diverse cultural knowledge.
VinaBench: Benchmark for Faithful and Consistent Visual Narratives
Visual narrative generation transforms textual narratives into sequences of images illustrating the content of the text.
Cross-Modal Learning for Music-to-Music-Video Description Generation
In this study, we focus on the MV description generation task and propose a comprehensive pipeline encompassing training data construction and multimodal model fine-tuning.
DeepResonance: Enhancing Multimodal Music Understanding via Music-centric Multi-way Instruction Tuning
Recent advancements in music large language models (LLMs) have significantly improved music understanding tasks, which involve the model's ability to analyze and interpret various musical elements.
TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation
This paper proposes a priority-based attention method to distinguish each turn explicitly by adding dialogue features into the attention mechanism, called Turn Emphasis with Dialogue (TED).
OpenMU: Your Swiss Army Knife for Music Understanding
We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music.
Distillation of Discrete Diffusion through Dimensional Correlations
Diffusion models have demonstrated exceptional performances in various fields of generative modeling, but suffer from slow sampling speed due to their iterative nature.
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
In each respective optimization step, the ranked prompts are fed as in-context examples (with their accuracies) to equip the LLM with the knowledge of the type of text prompts preferred by the downstream VLM.
ComperDial: Commonsense Persona-grounded Dialogue Dataset and Benchmark
We propose a new benchmark, ComperDial, which facilitates the training and evaluation of evaluation metrics for open-domain dialogue systems.
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing.
DiffuCOMET: Contextual Commonsense Knowledge Diffusion
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models.
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain
To address both these issues, we introduce a natural language inference method for post-hoc adapting a trained persona extraction model to a new setting.
On the Language Encoder of Contrastive Cross-modal Models
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them.
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives
They must also learn to maintain consistent speaker personas for themselves throughout the narrative, so that their counterparts feel involved in a realistic conversation or story.
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs.
