most citedComposerX: Multi-Agent Symbolic Music Composition with LLMs

6 citations · 33 across the 14 of their papers we have counts for

collaborators

14 papers

cs.AI2024

PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

Junjie Wang, Yuxiang Zhang, Minghao Liu +19

Recent advancements in large multimodal models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent ch…

cs.SD2024★ 6 cited

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…

cs.CL2024★ 5 cited

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…

cs.SD2024★ 4 cited

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…

cs.CL2024★ 3 cited

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…

cs.CL2024

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…

cs.CL2024★ 1 cited

StructLM: Towards Building Generalist Models for Structured Knowledge Grounding

Alex Zhuang, Ge Zhang, Tianyu Zheng +7

Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain tex…

cs.SE2024★ 4 cited

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

Tianyu Zheng, Ge Zhang, Tianhao Shen +5

The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of…

cs.CL2024

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…

cs.CL2024★ 3 cited

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

Jun Zhan, Junqi Dai, Jiasheng Ye +13

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images…

cs.IR2024

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…

cs.CL2024★ 3 cited

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…

cs.CL2024

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…

cs.CL2023★ 4 cited

Align on the Fly: Adapting Chatbot Behavior to Established Norms

Chunpu Xu, Steffi Chern, Ethan Chern +6

In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e.g., social norms) across time and locations. This presents a chall…