Papers by Chi Ma
Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)
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| Challenge: | Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts. |
| Approach: | They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG. |
| Outcome: | The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results. |
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models (2025.acl-long)
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Xinlin Zhuang, Jiahui Peng, Ren Ma, Yinfan Wang, Tianyi Bai, Xingjian Wei, Qiu Jiantao, Chi Zhang, Ying Qian, Conghui He
| Challenge: | composition of pre-training datasets for large language models remains undisclosed . current methods for evaluating data quality are limited by single-dimensional evaluation or redundancy-focused strategies. |
| Approach: | They propose a multi-dimensional data selection method that integrates dimensions with existing quality metrics through learned optimal weightings. |
| Outcome: | The proposed method doubles convergence speed for 1.3B model models and improves downstream task performance by 3.23%. |
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)
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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei
| Challenge: | ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive . |
| Approach: | They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora . |
| Outcome: | The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost. |
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder (2024.findings-naacl)
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| Challenge: | generative diversity is a critical yet underexplored issue in natural language generation . previous approaches to enhance diversity of Transformer models have been limited by their latent variables . |
| Approach: | They propose a framework that bridges Transformer with VAE to enhance generative diversity. |
| Outcome: | The proposed framework improves generative diversity while maintaining generative quality. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)
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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi
| Challenge: | Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions. |
| Approach: | They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them. |
| Outcome: | The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios. |
mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs (2021.emnlp-main)
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Zewen Chi, Li Dong, Shuming Ma, Shaohan Huang, Saksham Singhal, Xian-Ling Mao, Heyan Huang, Xia Song, Furu Wei
| Challenge: | Multilingual T5 pretrains a sequence-to-sequence model on monolingual texts, but it has shown promising results on many cross-lingual tasks. |
| Approach: | They propose a partially non-autoregressive objective for text-to-text pre-training and propose mT6 to improve cross-lingual transferability over multilingual T5. |
| Outcome: | The proposed model improves cross-lingual transferability over existing models. |
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)
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| Challenge: | Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses. |
| Approach: | They propose a new method that enables LLMs to self-rank their responses without additional resources. |
| Outcome: | The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods. |
Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)
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Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu
| Challenge: | Despite the success of speech recognition, how to encode the speech features effectively remains an open problem. |
| Approach: | They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. |
| Outcome: | The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x. |
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking (2021.emnlp-main)
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| Challenge: | Existing methods require training millions of architectures to estimate the accuracy of the search results. |
| Approach: | They propose a performance ranking method (RankNAS) that uses pairwise ranking and search space pruning to enlarge the search space. |
| Outcome: | The proposed method significantly accelerates NAS through pairwise ranking and search space pruning. |
Astra: Efficient Transformer Architecture and Contrastive Dynamics Learning for Embodied Instruction Following (2025.emnlp-main)
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| Challenge: | Existing vision-language-action models rely on causal attention for processing sequences composed of interleaved segments from different modalities. |
| Approach: | They propose a Transformer architecture featuring trajectory attention and learnable action queries that efficiently process segmented multimodal trajectories and predict actions for imitation learning. |
| Outcome: | The proposed architecture performs better on three large-scale robot manipulation benchmarks than previous models. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing methods for temporal knowledge graph embedding do not account for structural dependencies between relations. |
| Approach: | They propose a framework that enhances temporal knowledge graph completion through rule embedding. |
| Outcome: | The proposed framework improves temporal knowledge graph completion through rule embedding. |
Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs (2026.findings-eacl)
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| Challenge: | Existing sparsity methods lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. |
| Approach: | They propose a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability. |
| Outcome: | The proposed framework achieves 20% average speedup with less than 2% accuracy degradation outperforming Griffin and TT. |
CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)
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Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu
| Challenge: | Multimodal large language models have demonstrated promising results in a variety of tasks that combine vision and language. |
| Approach: | They propose a benchmark to assess the ability of models to use contextual information in free-form text to enhance visual comprehension. |
| Outcome: | The proposed model fails to extract and utilize contextual information to improve understanding of images. |
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)
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Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu
| Challenge: | Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops. |
| Approach: | They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty. |
| Outcome: | The proposed approach shows significant gains in both user satisfaction and exploration diversity. |
RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions (2026.acl-demo)
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| Challenge: | Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. |
| Approach: | They propose an open-source platform that passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer’s workflow. |
| Outcome: | The open-source platform captures 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. |
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022.coling-1)
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| Challenge: | Existing studies to translate medical jargon into layperson-understandable language focus on accuracy and readability aspects of clinical language. |
| Approach: | They propose to construct a dataset to support automated clinical language simplification and propose a model that mimics the human annotation procedure. |
| Outcome: | The proposed model matches human annotation procedures and achieves state-of-the-art performance compared with baselines. |
Scaling Collaborative Effort with Agents (2026.findings-acl)
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Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, David Sontag
| Challenge: | Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems. |
| Approach: | They propose a framework that captures how an agent’s utility grows with increasing user involvement. |
| Outcome: | The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. |