35 citations · 59 across the 25 of their papers we have counts for
7 papers · 1 filter
Hallucination Reduction for LLM-Based Audio Understanding via Multimodal Direct Preference Optimization
Bebe Cosgrove, Aaron Isidore Grace, Weiran Wang
Large Audio Language Models (LALMs) are prone to hallucinating and over-relying on text priors when simultaneously presented with audio and text inputs. To mitigate these hallucina…
Learning the Robustness Mechanism with Bilevel Optimization
Yiyang Shen, Qihang Lin, Weiran Wang
We propose a distributionally robust learning framework where parameters defining the robustness mechanism are learned from held-out data instead of extensively tuned. Using bileve…
Structured Latent Modeling for Supervised Multimodal Information Decomposition
Wanting Huang, Sanvesh Srivastava, Weiran Wang
Multimodal prediction relies on diverse forms of evidence: information repeated across modalities, cues specific to a single source, and complex cross-modal dependencies that emerg…
Catalogue of Solar Radio Bursts Detected by the LOFAR LV614 Station at the Irbene Observatory during Solar Cycle 25
Jānis Šteinbergs, Dmitrii Kolotkov, Valery M. Nakariakov +3
A catalogue (https://doi.org/10.5281/zenodo.20157853) of solar radio bursts detected with the LV614 LOFAR station at the Irbene Observatory, Latvia, in the stand-alone mode from Ju…
Adaptive Perturbation Selection for Contrastive Audio Decoding
Aaron Isidore Grace, Zhouyuan Huo, Weiran Wang
Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors. While contrastive decoding (CD) offers training-free mitigation, ex…
Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
Yiyang Shen, Yutian He, Weiran Wang +1
We study a class of bilevel optimization problems in which both the upper- and lower-level problems have minimax structures. This setting captures a broad range of emerging applica…
Align-Consistency: Improving Non-autoregressive and Semi-supervised ASR with Consistency Regularization
Wanting Huang, Weiran Wang
Consistency regularization (CR) improves the robustness and accuracy of Connectionist Temporal Classification (CTC) by ensuring predictions remain stable across input perturbations…