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Yuan Ge

7 accepted papers

2026

ATTENTION2PROBABILITY: ATTENTION-DRIVEN TERMINOLOGY PROBABILITY ESTIMATION FOR ROBUST SPEECH-TO-TEXT SYSTEM

ICASSP 2026poster

Recent advances in speech large language models (SLMs) have improved speech recognition and translation in general domains, but accurately generating domain-specific terms or neologisms remains challenging. To address this, we propose Attention2Probability: attention-driven terminology probability e…

Cited by 0SourcePDFScholar
2026

SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgement

AAAI 2026technical

Speech-to-Speech (S2S) Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling end-to-end spoken dialogue systems. However, evaluating these models remains a fundamental challenge. We propose SageLM, an end-to-end, multi-aspect, and explainable speech LLM for co

Cited by 0SourcePDFScholar
2026

WaveEx: Accelerating Flow Matching-based Speech Generation via Wavelet-guided Extrapolation

AAAI 2026technical

Flow matching-based generative models offer a principled approach to modeling continuous-time dynamics in speech generation. However, inference is often computationally expensive due to repeated neural network evaluations required by ODE solvers. We propose WaveEx, a training-free and plug-in accele

Cited by 0SourcePDFScholar
2025

A Modular-based Strategy for Mitigating Gradient Conflicts in Simultaneous Speech Translation

ICASSP 2025accepted

Simultaneous Speech Translation (SimulST) involves generating target language text while continuously processing streaming speech input, presenting significant real-time challenges. Multi-task learning is often employed to enhance SimulST performance but introduces optimization conflicts between pri…

Cited by 0SourceScholar
2025

Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models

EMNLP 2025

Test-Time Scaling (TTS) is a promising approach to progressively elicit the model’s intelligence during inference. Recently, training-based TTS methods, such as continued reinforcement learning (RL), have further surged in popularity, while training-free TTS methods are gradually fading from promine

2024

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation

EMNLP 2024main

With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required by training and evaluating models, it is advantageous to have an efficient method for selecting high-quality IT data. However, existing met…

2024

RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners

COLING 2024main

Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors during their reasoning processes. Existing solutions, such as deploying task-specific verifiers or voting over multiple re…

Cited by 4SourcePDFScholar