Papers with understanding and generation
Privacy-preserving Prosody Representation Learning (2026.acl-short)
Copied to clipboard
| Challenge: | Acoustic-prosodic cues are known to carry speaker information, exposing users to privacy breaches . a new self-supervised learning approach addresses privacy concerns . |
| Approach: | They propose a self-supervised approach to learning prosody representations that incorporates speaker disentanglement strategies. |
| Outcome: | The proposed model outperforms raw prosody and HuBERT-base baselines on three tasks . it achieves strong speaker disentanglement without adverse impact on prosody-related downstream tasks compared with baselines . |
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing. |
| Approach: | They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs. |
| Outcome: | The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
Copied to clipboard
Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
SparseFlow: Accelerating Transformers by Sparsifying Information Flows (2024.acl-long)
Copied to clipboard
| Challenge: | SparseFlow is an efficient method to sparsify the dense information flows within transformers. |
| Approach: | They propose a method to sparsify the dense pathways of token representations across all transformer blocks by parameterizing them to be sparse. |
| Outcome: | The proposed method reduces computational costs by half on average without compromising task accuracy. |
HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026.findings-acl)
Copied to clipboard
Jin Wang, Kaiwen Luo, Liang Lin, Weiliu Wang, Yitian Chen, Moayad Aloqaily, Xuehai Tang, Zhenhong Zhou, Kun Wang, Li Sun, Qingsong Wen
| Challenge: | Recent advances in audio large language models have led to their potential privacy implications unexplored. |
| Approach: | They propose a benchmark to examine whether ALLMs leak user privacy through acoustic voiceprints. |
| Outcome: | The proposed benchmark is constructed from over 22,000 real-world audio clips. |