47 citations · 312 across the 52 of their papers we have counts for
17 papers · 1 filter
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…
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Cong Wei, Yang Chen, Haonan Chen +5
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text description…
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Xiang Yue, Yuansheng Ni, Kai Zhang +19
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU…
Massive Editing for Large Language Models via Meta Learning
Chenmien Tan, Ge Zhang, Jie Fu
While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which…
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Xiang Yue, Xingwei Qu, Ge Zhang +5
We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on MathInstruct, o…
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks
Dongfu Jiang, Yishan Li, Ge Zhang +3
We present TIGERScore, a \textbf{T}rained metric that follows \textbf{I}nstruction \textbf{G}uidance to perform \textbf{E}xplainable, and \textbf{R}eference-free evaluation over a…
AutoAgents: A Framework for Automatic Agent Generation
Guangyao Chen, Siwei Dong, Yu Shu +5
Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on pr…
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Zihao Deng, Yinghao Ma, Yudong Liu +5
Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored. To address this…
On the Effectiveness of Speech Self-supervised Learning for Music
Yinghao Ma, Ruibin Yuan, Yizhi Li +12
Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR…
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
Le Zhuo, Ruibin Yuan, Jiahao Pan +10
We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets,…
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
Ruibin Yuan, Yinghao Ma, Yizhi Li +22
In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, partic…
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Yizhi Li, Ruibin Yuan, Ge Zhang +17
Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Althoug…
TPDM: Selectively Removing Positional Information for Zero-shot Translation via Token-Level Position Disentangle Module
Xingran Chen, Ge Zhang, Jie Fu
Due to Multilingual Neural Machine Translation's (MNMT) capability of zero-shot translation, many works have been carried out to fully exploit the potential of MNMT in zero-shot tr…
Interactive Natural Language Processing
Zekun Wang, Ge Zhang, Kexin Yang +19
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with t…
Chinese Open Instruction Generalist: A Preliminary Release
Ge Zhang, Yemin Shi, Ruibo Liu +9
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release…
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
Ruibo Liu, Chenyan Jia, Ge Zhang +3
We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and valu…
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
Ge Zhang, Yizhi Li, Yaoyao Wu +5
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer…