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Xiaojun Meng

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Difficulty Is Not Enough: Curriculum Learning for LLMs Fine-tuning Must Consider Utility

Zishang Jiang
Jinyi Han
Tingyun Li
Xinyi Wang
Sihang Jiang
Xiaojun Meng
Jiansheng Wei
Jiaqing Liang

Fine-tuning plays an essential role in improving the performance of large language models (LLMs) on specific tasks. A central challenge lies in designing data-efficient strategy to achieve better fine-tuning performance. Curriculum learning, which organizes data from easy to hard, has become a widely adopted technique in LLMs training. However, existing methods for curriculum learning focus only on the difficulty of samples, while neglecting their contribution to improving model performance, making them vulnerable when applied to fine-tuning LLMs. To address this, we propose Difficulty-Utility Curriculum Learning (DUCL), a curriculum learning framework that jointly considers difficulty and utility. DUCL introduces a novel scoring method, Difficulty-Utility Evaluation (DUE), and a soft scheduling strategy called Window Ordering, which together promote efficient and effective fine-tuning. Our method not only improves convergence and final performance with negligible computational overhead, but is also broadly applicable across a wide range of tasks, making it a practical and scalable solution for LLMs fine-tuning.

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AAAI Conference 2026 Conference Paper

MMIFEvol: Towards Evolutionary Multimodal Instruction Following

Haoyu Wang
Sihang Jiang
Xiangru Zhu
Yuyan Chen
Xiaojun Meng
Jiansheng Wei
Yitong Wang
Yanghua Xiao

Multimodal Instruction Following serves as a fundamental capability of multimodal language models, involving accurate comprehension and execution of user-provided instructions. However, existing multimodal instruction-following datasets and benchmarks face the shortcomings outlined below: (a) Lack of Difficulty Stratification, they collect diverse instruction categories but neglect the stratification of difficulty levels across these categories, which leads to overlap, bias, and low interpretability. (b) Lack of Fine-Grained Metrics, they conflate the model's ability to ``solve tasks" and ``follow constraints" into a single metric, which fails to accurately reflect its instruction-following capability. (c) Lack of Multi-Task Instructions, they overlook the fact that real-world user instructions often consist of multiple combined tasks. This paper proposes MMIFEvol, a framework for multimodal instruction evolving and benchmarking. First, we define the essential components of a carefully curated multimodal instruction set and establish corresponding difficulty levels, based on which we synthesize diverse instruction data. Next, we decouple the evaluation criteria for the instruction following into three different metrics to construct a high-quality benchmark and assess existing models. Experimental results demonstrate that current models still struggle with following complex instructions, while fine-tuning using MMIFEvol data effectively improves models' responsiveness to multimodal instructions.

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AAAI Conference 2025 Conference Paper

Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding

Yueqian Wang
Xiaojun Meng
Yuxuan Wang
Jianxin Liang
Qun Liu
Dongyan Zhao

Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used applications. Compared with the traditional multi-modal conversations, MMC requires stronger character-centered understanding abilities as there are many interlocutors appearing in both the visual and textual context. To facilitate the study of this problem, we present Friends-MMC in this paper, an MMC dataset that contains 24,000+ unique utterances paired with video context. To explore the character-centered understanding of the dialogue, we also annotate the speaker of each utterance, the names and bounding bboxes of faces that appear in the video. Based on this Friends-MMC dataset, we further study two fundamental MMC tasks: conversation speaker identification and conversation response prediction, both of which have the multi-party nature with the video or image as visual context. For conversation speaker identification, we demonstrate the inefficiencies of existing methods such as pre-trained models, and propose a simple yet effective baseline method that leverages an optimization solver to utilize the context of two modalities to achieve better performance. For conversation response prediction, we fine-tune generative dialogue models on Friend-MMC, and analyze the benefits of speaker information. The code and dataset will be publicly available, and thus we call for more attention on modelling speaker information when understanding conversations.

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ICML Conference 2025 Conference Paper

Sparsing Law: Towards Large Language Models with Greater Activation Sparsity

Yuqi Luo
Chenyang Song
Xu Han 0007
Yingfa Chen
Chaojun Xiao
Xiaojun Meng
Liqun Deng
Jiansheng Wei

Activation sparsity denotes the existence of substantial weakly-contributed neurons within feed-forward networks of large language models (LLMs), providing wide potential benefits such as computation acceleration. However, existing works lack thorough quantitative studies on this useful property, in terms of both its measurement and influential factors. In this paper, we address three underexplored research questions: (1) How can activation sparsity be measured more accurately? (2) How is activation sparsity affected by the model architecture and training process? (3) How can we build a more sparsely activated and efficient LLM? Specifically, we develop a generalizable and performance-friendly metric, named CETT-PPL-1%, to measure activation sparsity. Based on CETT-PPL-1%, we quantitatively study the influence of various factors and observe several important phenomena, such as the convergent power-law relationship between sparsity and training data amount, the higher competence of ReLU activation than mainstream SiLU activation, the potential sparsity merit of a small width-depth ratio, and the scale insensitivity of activation sparsity. Finally, we provide implications for building sparse and effective LLMs, and demonstrate the reliability of our findings by training a 2. 4B model with a sparsity ratio of 93. 52%, showing 4. 1$\times$ speedup compared with its dense version. The codes and checkpoints are available at https: //github. com/thunlp/SparsingLaw/.

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AAAI Conference 2024 Conference Paper

Unsupervised Extractive Summarization with Learnable Length Control Strategies

Renlong Jie
Xiaojun Meng
Xin Jiang
Qun Liu

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and PACSUM rely on graph-based ranking on sentence centrality. However, this scorer can not be directly applied in end-to-end training, and the positional-related prior assumption is often needed for achieving good summaries. In addition, less attention is paid to length-controllable extractor, where users can decide to summarize texts under particular length constraint. This paper introduces an unsupervised extractive summarization model based on a siamese network, for which we develop a trainable bidirectional prediction objective between the selected summary and the original document. Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption. In addition, we introduce a differentiable length control module by approximating 0-1 knapsack solver for end-to-end length-controllable extracting. Experiments show that our unsupervised method largely outperforms the centrality-based baseline using a same sentence encoder. In terms of length control ability, via our trainable knapsack module, the performance consistently outperforms the strong baseline without utilizing end-to-end training. Human evaluation further evidences that our method performs the best among baselines in terms of relevance and consistency.

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IJCAI Conference 2023 Conference Paper

Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video

Zenan Xu
Xiaojun Meng
Yasheng Wang
Qinliang Su
Zexuan Qiu
Xin Jiang
Qun Liu

Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of the large-scale generative pre-trained language model (GPLM) in generating high-quality textual content (e. g. , summary), recent MAS methods have proposed to adapt the GPLM to this task by equipping it with the visual information, which is often obtained through a general-purpose visual feature extractor. However, the generally extracted visual features may overlook some summary-worthy visual information, which impedes model performance. In this work, we propose a novel approach to learning the summary-worthy visual representation that facilitates abstractive summarization. Our method exploits the summary-worthy information from both the cross-modal transcript data and the knowledge that distills from the pseudo summary. Extensive experiments on three public multimodal datasets show that our method outperforms all competing baselines. Furthermore, with the advantages of summary-worthy visual information, our model can have a significant improvement on small datasets or even datasets with limited training data.

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AAAI Conference 2022 Conference Paper

UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation

Zhengkun Zhang
Xiaojun Meng
Yasheng Wang
Xin Jiang
Qun Liu
Zhenglu Yang

With the rapid increase of multimedia data, a large body of literature has emerged to work on multimodal summarization, the majority of which target at refining salient information from textual and visual modalities to output a pictorial summary with the most relevant images. Existing methods mostly focus on either extractive or abstractive summarization and rely on qualified image captions to build image references. We are the first to propose a Unified framework for Multimodal Summarization grounding on BART, UniMS, that integrates extractive and abstractive objectives, as well as selecting the image output. Specially, we adopt knowledge distillation from a vision-language pretrained model to improve image selection, which avoids any requirement on the existence and quality of image captions. Besides, we introduce a visual guided decoder to better integrate textual and visual modalities in guiding abstractive text generation. Results show that our best model achieves a new state-of-the-art result on a large-scale benchmark dataset. The newly involved extractive objective as well as the knowledge distillation technique are proven to bring a noticeable improvement to the multimodal summarization task.

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NeurIPS Conference 2022 Conference Paper

Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark

Jiaxi Gu
Xiaojun Meng
Guansong Lu
Lu Hou
Niu Minzhe
Xiaodan Liang
Lewei Yao
Runhui Huang

Vision-Language Pre-training (VLP) models have shown remarkable performance on various downstream tasks. Their success heavily relies on the scale of pre-trained cross-modal datasets. However, the lack of large-scale datasets and benchmarks in Chinese hinders the development of Chinese VLP models and broader multilingual applications. In this work, we release a large-scale Chinese cross-modal dataset named Wukong, which contains 100 million Chinese image-text pairs collected from the web. Wukong aims to benchmark different multi-modal pre-training methods to facilitate the VLP research and community development. Furthermore, we release a group of models pre-trained with various image encoders (ViT-B/ViT-L/SwinT) and also apply advanced pre-training techniques into VLP such as locked-image text tuning, token-wise similarity in contrastive learning, and reduced-token interaction. Extensive experiments and a benchmarking of different downstream tasks including a new largest human-verified image-text test dataset are also provided. Experiments show that Wukong can serve as a promising Chinese pre-training dataset and benchmark for different cross-modal learning methods. For the zero-shot image classification task on 10 datasets, $Wukong_\text{ViT-L}$ achieves an average accuracy of 73. 03%. For the image-text retrieval task, it achieves a mean recall of 71. 6% on AIC-ICC which is 12. 9% higher than WenLan 2. 0. Also, our Wukong models are benchmarked on downstream tasks with other variants on multiple datasets, e. g. , Flickr8K-CN, Flickr-30K-CN, COCO-CN, et al. More information can be referred to https: //wukong-dataset. github. io/wukong-dataset/.

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