Papers by Ke Ma
A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have succeeded in natural language processing because they learn generic knowledge from a large corpus. |
| Approach: | They propose a method that allows pre-trained language models to explore simile knowledge from PLMs . they enhance PLM models with a multi-level simile recognition task that evaluates similes aplenty . |
| Outcome: | The proposed method can explore more accurate simile knowledge for PLMs. |
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)
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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
| Challenge: | Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it . |
| Approach: | They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers. |
| Outcome: | The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy . |
DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation (2024.lrec-main)
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| Challenge: | Extensive experiments on three user-specific speech-to-text tasks show that DOC-RAG significantly outperforms strong baselines with an 8-15% improvement in terms of perplexity and a 4-7% reduction in terms in terms . of Word Error Rates. |
| Approach: | They propose a domain-distributed co-occurrence augmentation approach to improve automatic speech recognition of rare word patterns in unseen domains by using n-gram co-existence distributions. |
| Outcome: | Experiments on three user-specific speech-to-text tasks show that DOC-RAG outperforms baselines with an 8-15% improvement in terms of perplexity and a 4-7% reduction in terms in terms . of Word Error Rates. |
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)
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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang
| Challenge: | Existing tools for detecting safety issues in LLMs are expensive and inefficient. |
| Approach: | They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions. |
| Outcome: | The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs. |
Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)
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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu
| Challenge: | Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence. |
| Approach: | They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs. |
| Outcome: | The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks. |
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics (2023.acl-long)
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Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, but they are insufficient for diagnosing whether metrics are consistent, effective on human-written texts, and sensitive to different error types. |
| Approach: | They propose to use unfaithful minimal pairs to measure the consistency of automatic faithfulness metrics by comparing human-written summary pairs with a dataset of 889 human-writing, minimally different summary pairs. |
| Outcome: | The proposed benchmarks show that the most discriminative metrics tend not to be the most consistent, and that the best performing metrics are sensitive to errors. |
A Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy (2025.emnlp-industry)
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| Challenge: | Existing methods for abnormal event detection face two predominant limitations . existing methods rely on specialized small models and are limited by performance bottlenecks . |
| Approach: | They propose a framework that leverages the advanced reasoning capabilities of large language models for abnormal event detection. |
| Outcome: | The proposed framework achieves the highest F1 score and an average improvement of 9.59% in OOD transfer tests. |
Adaptive Contrastive Knowledge Distillation for BERT Compression (2023.findings-acl)
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| Challenge: | Existing knowledge distillation methods for BERT implicitly learn discriminative student features by mimicking the teacher features. |
| Approach: | They propose a new knowledge distillation approach called adaptive contrastive knowledge distilling for BERT compression using hidden state features in BERT as explicit supervision to learn discriminative student features. |
| Outcome: | The proposed approach improves on multiple natural language processing tasks. |
PaTaRM: Bridging Pairwise and Pointwise Signals via Preference-Aware Task-Adaptive Reward Modeling (2026.acl-long)
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| Challenge: | Existing reward models lack generative and reasoning capabilities, resulting in poor performance. |
| Approach: | They propose a reward-aware task-adaptive reward model that enables pointwise training using readily available pairwise data via a novel Preference-Aware Reward mechanism. |
| Outcome: | The proposed reward model achieves an average relative improvement of 8.7% over the base models on RewardBench and RMBench. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning (2025.acl-long)
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Xiaoqian Liu, Ke Wang, Yongbin Li, Yuchuan Wu, Wentao Ma, Aobo Kong, Fei Huang, Jianbin Jiao, Junge Zhang
| Challenge: | Existing methods for strategic reasoning face challenges in adaptability, scalability, and transferring strategies to new contexts. |
| Approach: | They propose an explicit policy optimization model that provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior. |
| Outcome: | The proposed model provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior. |
SDPO: Segment-Level Direct Preference Optimization for Social Agents (2025.acl-long)
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Aobo Kong, Wentao Ma, Shiwan Zhao, Yongbin Li, Yuchuan Wu, Ke Wang, Xiaoqian Liu, Qicheng Li, Yong Qin, Fei Huang
| Challenge: | Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across various tasks, but is limited in multi-turn social interactions. |
| Approach: | They propose a method which dynamically selects key segments within interactions to optimize multi-turn agent behavior. |
| Outcome: | The proposed methods outperform existing methods and proprietary LLMs on the SOTOPIA benchmark and show that they can improve social intelligence. |
Beta-LR: Interpretable Logical Reasoning based on Beta Distribution (2024.findings-naacl)
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| Challenge: | Existing methods to capture logical information from text are limited by the uncertainty of the text. |
| Approach: | They propose a probabilistic embedding method to capture logical information from text . they embed texts into beta distributions on each dimension to eliminate logical uncertainty . |
| Outcome: | The proposed method achieves competitive performances on two datasets. |
Dynamic Open-book Prompt for Conversational Recommender System (2023.findings-emnlp)
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| Challenge: | Existing methods for prompt learning use only training samples for parameter training, limiting the performance of existing methods. |
| Approach: | They propose a Dynamic Open-book Prompt approach where the open book stores user's experiences in historical data and dynamically constructs the prompt to memorize the user' s current utterance. |
| Outcome: | The proposed model improves on the existing methods on the ReDial dataset and shows that it can be used to learn contextually relevant recommendations. |
When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning (2025.findings-emnlp)
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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, Xunliang Cai
| Challenge: | Large reasoning models (LRMs) incur excessive computational overhead due to redundant reasoning, especially on simple tasks. |
| Approach: | They propose an Adaptive Self-Recovery Reasoning framework that suppresses unnecessary reasoning and enables implicit recovery. |
| Outcome: | The proposed framework suppresses unnecessary reasoning and enables implicit recovery. |
PersonaLM: Language Model Personalization via Domain-distributed Span Aggregated K-Nearest N-gram Retrieval Augmentation (2023.findings-emnlp)
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| Challenge: | Existing language modeling tools for automatic speech recognition (ASR) are difficult to personalize. |
| Approach: | They propose a domain-distributed Span-Aggregated K-nearest N-gram retrieval augmentation to improve language modeling for automatic speech recognition (ASR) personalization. |
| Outcome: | The proposed model outperforms baselines on Wikitext-103, UserLibri, and ASAP datasets with a 10-16% improvement in perplexity and a 5-8% reduction in word error rates. |
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)
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Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao
| Challenge: | High-quality data in training proactive dialogue agents is scarce, despite fine-tuning and reinforcement learning . a recent study has shown that the effectiveness of supervised fine-touring is limited by the lack of high-quality, domain-specific training data. |
| Approach: | They propose a framework for training recruitment proactive dialogue agents using a high-fidelity user simulator and a multi-dimensional evaluation framework based on Chain-of-Intention. |
| Outcome: | The proposed framework outperforms existing simulator-based data selection strategies in a real-world recruitment scenario. |
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models (2025.acl-long)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a common method for fine- tuning large language models . however, once updated, PEFT modules suffer performance degradation on newer versions . |
| Approach: | They propose a method that enhances the PEFT module by focusing on the task-specific pattern while reducing its dependence on certain knowledge in the base model. |
| Outcome: | Experiments show that PEFT modules can maintain performance on updated models without re-tuning . the proposed approach can be used in real-world applications with large model sizes . |
FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents (2024.findings-emnlp)
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| Challenge: | LLM-based agents are susceptible to undesired planning hallucinations when lacking specific knowledge for expertise-intensive tasks. |
| Approach: | They propose a benchmark to evaluate the efficacy of workflow-guided agent planning by formalizing different formats of workflow knowledge. |
| Outcome: | The proposed benchmark aims to improve the planning reliability of LLM-based agents by incorporating external workflow-related knowledge. |
I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023.findings-acl)
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| Challenge: | A simile is a figure of speech that compares two different things via shared properties. |
| Approach: | They propose a multilingual simile dialogue dataset that can be used to study similes in real-life scenarios. |
| Outcome: | The proposed dataset is the largest manually annotated simile dataset and contains both English and Chinese data. |