1 citations · 4 across the 18 of their papers we have counts for
19 papers
RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding
Zihong Zhang, Zuchao Li, Lefei Zhang +2
Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigates this through a guess-and-v…
SongSage: A Large Musical Language Model with Lyric Generative Pre-training
Jiani Guo, Jiajia Li, Jie Wu +3
Large language models have achieved significant success in various domains, yet their understanding of lyric-centric knowledge has not been fully explored. In this work, we first i…
MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment
Yupeng Cao, Chengyang He, Yangyang Yu +2
Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial progress in automated veracity assessment,…
End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering
Jiliang Hu, Zuchao Li, Baoyuan Qi +2
Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio language models, struggle with processi…
INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance
Shisong Chen, Qian Zhu, Wenyan Yang +15
Insurance, as a critical component of the global financial system, demands high standards of accuracy and reliability in AI applications. While existing benchmarks evaluate AI capa…
SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers
Zicong Tang, Shi Luohe, Zuchao Li +4
Large Language Models (LLMs) have achieved impressive accomplishments in recent years. However, the increasing memory consumption of KV cache has possessed a significant challenge…
From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs
Guocong Li, Weize Liu, Yihang Wu +4
Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these…
Faster MoE LLM Inference for Extremely Large Models
Haoqi Yang, Luohe Shi, Qiwei Li +5
Sparse Mixture of Experts (MoE) large language models (LLMs) are gradually becoming the mainstream approach for ultra-large-scale models. Existing optimization efforts for MoE mode…
Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction
Lu Yang, Jiajia Li, En Ci +3
Universal Information Extraction (UIE) has garnered significant attention due to its ability to address model explosion problems effectively. Extractive UIE can achieve strong perf…
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model
Mingni Tang, Jiajia Li, Lu Yang +5
Symbolic music is represented in two distinct forms: two-dimensional, visually intuitive score images, and one-dimensional, standardized text annotation sequences. While large lang…
VHASR: A Multimodal Speech Recognition System With Vision Hotwords
Jiliang Hu, Zuchao Li, Ping Wang +3
The image-based multimodal automatic speech recognition (ASR) model enhances speech recognition performance by incorporating audio-related image. However, some works suggest that i…
A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction
Xiangke Zeng, Zuchao Li, Lefei Zhang +3
Chinese Spelling Correction (CSC) stands as a foundational Natural Language Processing (NLP) task, which primarily focuses on the correction of erroneous characters in Chinese text…
Hypergraph based Understanding for Document Semantic Entity Recognition
Qiwei Li, Zuchao Li, Ping Wang +2
Semantic entity recognition is an important task in the field of visually-rich document understanding. It distinguishes the semantic types of text by analyzing the position relatio…
The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models
Jiajia Li, Lu Yang, Mingni Tang +4
Benchmark plays a pivotal role in assessing the advancements of large language models (LLMs). While numerous benchmarks have been proposed to evaluate LLMs' capabilities, there is…
Multi-modal Auto-regressive Modeling via Visual Words
Tianshuo Peng, Zuchao Li, Lefei Zhang +3
Large Language Models (LLMs), benefiting from the auto-regressive modelling approach performed on massive unannotated texts corpora, demonstrates powerful perceptual and reasoning…
N-Gram Unsupervised Compoundation and Feature Injection for Better Symbolic Music Understanding
Jinhao Tian, Zuchao Li, Jiajia Li +1
The first step to apply deep learning techniques for symbolic music understanding is to transform musical pieces (mainly in MIDI format) into sequences of predefined tokens like no…
A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis
Tianshuo Peng, Zuchao Li, Ping Wang +2
Multi-modal aspect-based sentiment analysis (MABSA) has recently attracted increasing attention. The span-based extraction methods, such as FSUIE, demonstrate strong performance in…
Multi-modal Latent Space Learning for Chain-of-Thought Reasoning in Language Models
Liqi He, Zuchao Li, Xiantao Cai +1
Chain-of-thought (CoT) reasoning has exhibited impressive performance in language models for solving complex tasks and answering questions. However, many real-world questions requi…
ArcMMLU: A Library and Information Science Benchmark for Large Language Models
Shitou Zhang, Zuchao Li, Xingshen Liu +2
In light of the rapidly evolving capabilities of large language models (LLMs), it becomes imperative to develop rigorous domain-specific evaluation benchmarks to accurately assess…