Papers by Bo Du
OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | None Large language models (LLMs) are emerging as a key tool for automated programming. |
| Approach: | They compare performance of None Large language models with language understanding models on functional programming and object-oriented programming benchmarks. |
| Outcome: | The models perform relatively well on functional programming (FP) and object-oriented programming (OOP) benchmarks, while exhibiting poor performance on OOP benchmarks. |
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
Tchebycheff Procedure for Multi-task Text Classification (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for text classification assume that multitask text classification problems are convex multiobjective optimization problems. |
| Approach: | They propose a Tchebycheff procedure to optimize multi-task classification problems without convex assumption. |
| Outcome: | The proposed method is able to find an arbitrary Pareto optimal solution in the PareTO set if the problem is convex, but excludes many Paret optimal solutions from its search scope. |
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment (2025.findings-acl)
Copied to clipboard
| Challenge: | Medical dialogue systems (MDS) struggle to identify relevant medical knowledge and generate accurate responses. |
| Approach: | They propose a medical dialogue system that integrates knowledge refining and dynamic prompt adjustment to improve medical knowledge and accuracy. |
| Outcome: | The proposed system outperforms state-of-the-art systems in both generation quality and medical entity accuracy. |
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
Copied to clipboard
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
Revisiting Knowledge Distillation for Autoregressive Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Autoregressive language models (LMs) are expensive and memory intensive, preventing the development of industrial applications. |
| Approach: | They propose an adaptive teaching approach to improve the KD of autoregressive language models by distilling knowledge into a small student model. |
| Outcome: | The proposed method can achieve consistent and significant performance gains across all model types and sizes. |
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)
Copied to clipboard
Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao
| Challenge: | Recent methods focus on search accuracy while overlooking computational efficiency. |
| Approach: | They propose a parallelism framework that dynamically optimizes reasoning path in inference. |
| Outcome: | The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy. |
Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)
Copied to clipboard
| Challenge: | Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT. |
| Approach: | They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers. |
| Outcome: | The proposed method achieves consistent and significant performance gains across all tasks and model sizes. |
LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs. |
| Approach: | They propose a model that uses a constant-sized key-value cache to train long-context models. |
| Outcome: | Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks. |
Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)
Copied to clipboard
Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu
| Challenge: | Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model. |
| Approach: | They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures. |
| Outcome: | The proposed framework yields significant performance gains on Twitter and other platforms. |
Boosting Text-to-SQL through Multi-grained Error Identification (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for error identification often overlook validation of generated results . text-to-SQL is a technology that converts natural language questions into executable SQL queries . |
| Approach: | They propose to integrate a multi-grained error identification method into existing methods to detect SQL errors. |
| Outcome: | The proposed method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities. |
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)
Copied to clipboard
Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao
| Challenge: | Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning . |
| Approach: | They propose a synergistic dual-process framework that integrates reasoning and retrieval. |
| Outcome: | The proposed framework improves answer accuracy and coherence even in smaller-scale models. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
Copied to clipboard
Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)
Copied to clipboard
| Challenge: | Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance. |
| Approach: | They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples . |
| Outcome: | The proposed method is able to support global and heterogeneous pruning without back-propagation. |
CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4V (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances in generative AI have suggested that by taking visual prompts, GPT-4V can demonstrate significant proficiency in visual recognition tasks. |
| Approach: | They propose a collage prompting task that collages multiple images into a single visual prompt and makes GPT-4V perform visual recognition on several images simultaneously. |
| Outcome: | The proposed task reduces the cost associated with GPT-4V's visual recognition . the proposed task group images of the same category together leads to better visual recognition results . |
KaFT: Knowledge-aware Fine-tuning for Boosting LLMs’ Domain-specific Question-Answering Performance (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent literature reveals that supervised fine-tuning (SFT) is suboptimal for domain-specific question-answering tasks. |
| Approach: | They propose a query diversification strategy for robust conflict detection and a knowledge-aware fine-tuning approach to effectively boost LLMs’ performance. |
| Outcome: | The proposed approach improves the model generalization and alleviates the hallucination. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
Copied to clipboard
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. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
Copied to clipboard
Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
ROSE Doesn’t Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for aligning LLMs output with expected safety require substantial training efforts and expensive computational resources. |
| Approach: | They propose a method to directly boost the safety of existing instruction-tuned large language models without additional training. |
| Outcome: | The proposed method improves safety of instruction-tuned large language models without training and requires expensive computational resources. |
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience. |
| Approach: | They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications . |
| Outcome: | a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance . |
Planning with Diffusion Models for Target-Oriented Dialogue Systems (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for directing conversations toward specific targets generate dialogue plans in a step-by-step sequential manner and suffer from compounding errors and myopic actions. |
| Approach: | They propose a framework that leverages diffusion models to enable non-sequential dialogue planning. |
| Outcome: | The proposed framework performs non-myopic lookahead exploration and optimizes action strategies over a long horizon through non-sequential dialogue planning. |
From Parameters to Performance: A Data-Driven Study on LLM Structure and Development (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models have revolutionized a wide range of domains, driving significant advancements in both technology and real-world applications. |
| Approach: | They present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks. |
| Outcome: | The proposed model validates the relationship between structural configurations and performance across multiple benchmarks and further corroborates the findings using mechanistic interpretability techniques. |
Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)
Copied to clipboard
Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying
| Challenge: | Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks . |
| Approach: | They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication. |
| Outcome: | The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges. |
| Approach: | They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries . |
| Outcome: | The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs. |
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to learn multi-modal tasks are based on chain-of-thought . however, human thought processes are non-linear and employ dynamic adjustment and updating mechanisms. |
| Approach: | They propose a chain-of-thought technique that adjusts the length of the chain to improve the performance of generated prompts. |
| Outcome: | The proposed model improves multi-modal representation learning in visual, visual, and audio-visual tasks and also has good domain generalization performance due to better reasoning. |
Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)
Copied to clipboard
| Challenge: | Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context. |
| Approach: | They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image . |
| Outcome: | The proposed method can be integrated into existing models and demonstrate consistent performance improvements. |
Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation. |
| Approach: | They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback. |
| Outcome: | The proposed method achieves state-of-the-art on seven semantic text similarity tasks. |
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing text-to-SQL LLMs are computationally expensive and difficult to deploy in real-world applications. |
| Approach: | They propose to distill a larger teacher model into a smaller student model by using imperfect data to improve the KD. |
| Outcome: | The proposed method achieves the best tradeoff between performance and efficiency on 5 text-to-SQL benchmarks. |
PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing retrieval-augmented generation systems employ rigid retrieval strategies . static retrieval produces knowledge blind spots, missing connections between quantum algorithms and encryption vulnerabilities . |
| Approach: | PathwiseRAG addresses these challenges through intent-aware strategy selection . it constructs a directed acyclic graph of interconnected sub-problems and explores multiple reasoning trajectories . |
| Outcome: | The proposed framework achieves higher accuracy and better reliability than current systems. |
SAE-SSV: Supervised Steering in Sparse Representation Spaces for Reliable Control of Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive capabilities in natural language understanding and generation, but controlling their behavior remains a challenge. |
| Approach: | They propose a supervised steering approach that operates in sparse, interpretable representation spaces. |
| Outcome: | The proposed approach achieves higher success rates with minimal degradation in generation quality compared to existing methods. |
Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts (2022.coling-1)
Copied to clipboard
| Challenge: | Recent multimodal information extraction approaches overestimate the significance of images. |
| Approach: | They propose a general data splitting strategy to divide social media posts into two sets to achieve better performance under information extraction models of the corresponding modalities. |
| Outcome: | The proposed method outperforms existing models on two different multimodal information extraction tasks. |
Self-Evolution Learning for Discriminative Language Model Pretraining (2023.findings-acl)
Copied to clipboard
| Challenge: | Random masking does not consider the importance of the different words in the sentence meaning, e.g., entity-level masking requires expensive prior knowledge and generally does not use existing model weights. |
| Approach: | They propose a token masking and learning method that uses a random masking strategy to learn the under-explored tokens. |
| Outcome: | The proposed method improves linguistic knowledge learning and generalization on 10 tasks. |
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pretrained language models suffer from poor generalization . however, they add a perturbation to each model parameter equally, which is sub-optimal . |
| Approach: | They propose a sharpness-aware minimization optimization procedure that introduces a Fisher mask to improve the efficiency of SAM. |
| Outcome: | The proposed method outperforms the vanilla sharpness-aware minimization method on GLUE and SuperGLUE benchmarks. |
GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Chain-of-Thought reasoning suffers from an inherent mechanism flaw: linearity induces overthinking . emergence of Large Language Models (LLMs) has fundamentally redefined artificial intelligence . |
| Approach: | They propose a framework that replaces verbose linear trajectories with high-density reasoning graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art models with reduced token overhead. |
VC4VG: Optimizing Video Captions for Text-to-Video Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in text-to-video generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-aligned videos. |
| Approach: | They propose a caption optimization framework tailored to the needs of T2V models. |
| Outcome: | The proposed framework improves video caption quality and video generation performance. |
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods train RL-based agents with greedy action selection or sampling strategy and suffer from suboptimal conversational planning. |
| Approach: | They propose a Monte Carlo Tree Search-based CRS framework called SAPIENT . it consists of a conversational agent and a communication planner . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on four benchmark datasets. |
Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to measure word segmentation only assess the language model's understanding of the overall meaning of sentences, lacking an evaluation of the language models' understanding capabilities at a fine-grained level. |
| Approach: | They propose a framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) they employ current mainstream LLMs to perform word segmentations across multiple languages . |
| Outcome: | The proposed method improves on existing methods and combines the advanced pattern recognition capabilities of Aho-Corasick automata with the deep insights of well-pretrained LLMs. |
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for steering concept vectors suffer from noisy features in diverse datasets that undermine steering robustness. |
| Approach: | They propose a Sparse Autoencoder-Denoised Concept Vector (SDCV) which selectively keeps the most discriminative SAE latents while reconstructing hidden representations. |
| Outcome: | The proposed method improves steering success rates by 4-16% across six challenging concepts while maintaining topic relevance. |
DNASpeech: A Contextualized and Situated Text-to-Speech Dataset with Dialogues, Narratives and Actions (2025.acl-long)
Copied to clipboard
| Challenge: | Existing TTS datasets lack situated descriptive prompts aligned with speech data. |
| Approach: | They propose a contextualized and situated text-to-speech task to promote more accurate and customized speech generation using DNA prompts. |
| Outcome: | The proposed task promotes more accurate and customized speech generation using DNA prompts. |