Authors

* External authors

Venue

Date

Share

DiffuCOMET: Contextual Commonsense Knowledge Diffusion

Silin Gao*

Mete Ismayilzada*

Mengjie Zhao*

Hiromi Wakaki*

Yuki Mitsufuji

Antoine Bosselut*

* External authors

ACL-24

2024

Abstract

Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, DiffuCOMET, that leverage diffusion to learn to reconstruct the implicit semantic connections between narrative contexts and relevant commonsense knowledge. Across multiple diffusion steps, our method progressively refines a representation of commonsense facts that is anchored to a narrative, producing contextually-relevant and diverse commonsense inferences for an input context. To evaluate DiffuCOMET, we introduce new metrics for commonsense inference that more closely measure knowledge diversity and contextual relevance. Our results on two different benchmarks, ComFact and WebNLG+, show that knowledge generated by DiffuCOMET achieves a better trade-off between commonsense diversity, contextual relevance and alignment to known gold references, compared to baseline knowledge models.

Related Publications

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models

ICCV, 2025
Zerui Tao, Yuhta Takida, Naoki Murata, Qibin Zhao*, Yuki Mitsufuji

Parameter-Efficient Fine-Tuning (PEFT) of text-to-image models has become an increasingly popular technique with many applications. Among the various PEFT methods, Low-Rank Adaptation (LoRA) and its variants have gained significant attention due to their effectiveness, enabl…

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

Interspeech, 2025
Yigitcan Özer, Woosung Choi, Joan Serrà, Mayank Kumar Singh*, Wei-Hsiang Liao, Yuki Mitsufuji

We introduce the Robust Audio Watermarking Benchmark (RAW-Bench), a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic comparisons. To simulate real-world usage, we introduce a comprehensive audio attack pipeline with var…

Training Consistency Models with Variational Noise Coupling

ICML, 2025
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji

Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and impr…

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.