12 citations · 51 across the 38 of their papers we have counts for
21 papers · 1 filter
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
Siwei Wu, Zhongyuan Peng, Xinrun Du +14
Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, mer…
Can MLLMs Understand the Deep Implication Behind Chinese Images?
Chenhao Zhang, Xi Feng, Yuelin Bai +18
As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…
SongTrans: An unified song transcription and alignment method for lyrics and notes
Siwei Wu, Jinzheng He, Ruibin Yuan +5
The quantity of processed data is crucial for advancing the field of singing voice synthesis. While there are tools available for lyric or note transcription tasks, they all need p…
Foundation Models for Music: A Survey
Yinghao Ma, Anders Øland, Anton Ragni +39
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…
OmniBench: Towards The Future of Universal Omni-Language Models
Yizhi Li, Yinghao Ma, Ge Zhang +20
Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concu…
Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation
Xingwei Qu, Ge Zhang, Siwei Wu +2
This paper presents the results of the shared task on Chinese metaphor generation, hosted at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 202…
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models
Siwei Wu, Kang Zhu, Yu Bai +10
Given the remarkable success that large visual language models (LVLMs) have achieved in image perception tasks, the endeavor to make LVLMs perceive the world like humans is drawing…
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Yiming Liang, Ge Zhang, Xingwei Qu +9
Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potenti…
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
Shun Wang, Ge Zhang, Han Wu +3
Machine Translation (MT) has developed rapidly since the release of Large Language Models and current MT evaluation is performed through comparison with reference human translation…
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
Ge Zhang, Scott Qu, Jiaheng Liu +42
Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most comp…
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
Qixin Deng, Qikai Yang, Ruibin Yuan +16
Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and har…
MuPT: A Generative Symbolic Music Pretrained Transformer
Xingwei Qu, Yuelin Bai, Yinghao Ma +25
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our f…
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Yuelin Bai, Xinrun Du, Yiming Liang +19
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instructi…
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
Xingwei Qu, Yiming Liang, Yucheng Wang +10
Large Language Models (LLMs) exhibit the ability to perform in-context learning (ICL), where they acquire new tasks directly from examples provided in demonstrations. This process…
ChatMusician: Understanding and Generating Music Intrinsically with LLM
Ruibin Yuan, Hanfeng Lin, Yi Wang +32
While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative langua…
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
Yujie Shao, Xinrong Yao, Xingwei Qu +5
Metaphor is a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. This paper introduces a lar…
Pixel Sentence Representation Learning
Chenghao Xiao, Zhuoxu Huang, Danlu Chen +7
Pretrained language models are long known to be subpar in capturing sentence and document-level semantics. Though heavily investigated, transferring perturbation-based methods from…
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
Yizhi LI, Ge Zhang, Xingwei Qu +16
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-fol…
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
Siwei Wu, Yizhi Li, Kang Zhu +11
Multi-modal information retrieval (MMIR) is a rapidly evolving field, where significant progress, particularly in image-text pairing, has been made through advanced representation…
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
Ge Zhang, Xinrun Du, Bei Chen +19
As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger g…
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
Tianyu Zheng, Shuyue Guo, Xingwei Qu +7
In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. Adap…