Papers by Yong Dai
NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging (2025.emnlp-main)
Copied to clipboard
Weiming Zhang, Qingyao Li, Xinyi Dai, Jizheng Chen, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Early debugging efforts focused on code-level analysis, which often fails when addressing complex programming errors. |
| Approach: | They propose a framework that employs natural language as an intermediate representation to improve code debugging by debuggating at a natural language level. |
| Outcome: | The proposed framework outperforms traditional debugging methods and enables a broader modification space through direct refinement guided by execution feedback. |
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved. |
| Approach: | They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion. |
| Outcome: | The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0. |
DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation (2025.acl-long)
Copied to clipboard
Jizheng Chen, Kounianhua Du, Xinyi Dai, Weiming Zhang, Xihuai Wang, Yasheng Wang, Ruiming Tang, Weinan Zhang, Yong Yu
| Challenge: | Existing debate-based approaches to code generation are limited due to several reasons: 1) Reliance on different instances of the same LLM for debate, 2) under-utilization of test cases, and 3) reliance on third-party moderators for result consolidation and decision-making. |
| Approach: | They propose to use test cases to analyze code and identify bugs while opposing models generate test cases for each other to challenge each other's code during the debate process. |
| Outcome: | The proposed model collects intelligence of LLMs via test case-driven debate for code generation. |
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for training large language models require additional annotations to adjust to shifted distributions. |
| Approach: | They propose an algorithm that allows LLMs and reward models to update alternatively via a min-max game to improve their alignment. |
| Outcome: | The proposed framework improves existing alignment baselines in terms of LLM helpfulness and harmlessness. |
Leveraging Only the Category Name for Aspect Detection through Prompt-based Constrained Clustering (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Aspect category detection (ACD) aims to automatically identify user-concerned aspects from online reviews. |
| Approach: | They propose a method that relies on the category name of each aspect and a pretrained language model to generate constraints for clustering. |
| Outcome: | The proposed framework performs better than existing weakly supervised methods on nine benchmark datasets. |
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation (2025.findings-acl)
Copied to clipboard
Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang
| Challenge: | Existing systems 2 methods for code generation are difficult to implement due to the complex hidden reasoning process and heterogeneous data distribution. |
| Approach: | They propose a framework that Boosts reasoning exploration via multi-agent collaboration and Disentangles heterogeneous data into specialized experts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on APPS and CodeContest benchmarks and achieves 73.8% accuracy on hard problems. |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs. |
| Approach: | They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods . |
| Outcome: | The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption. |
Exploring and Adapting Chinese GPT to Pinyin Input Method (2022.acl-long)
Copied to clipboard
| Challenge: | a frozen GPT can generate state-of-the-art performance on perfect pinyin, but performance drops when input includes abbreviated pinyan, which links to even larger number of Chinese characters. |
| Approach: | They propose to use Chinese GPT to generate fluent sentences using abbreviated pinyin. |
| Outcome: | The proposed approach improves on abbreviated pinyin across all domains. |
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing web agents only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios. |
| Approach: | They propose a large multimodal model-powered web agent that can complete user instructions end-to-end by interacting with real-world websites. |
| Outcome: | The proposed agent achieves 59.1% task success rate, surpassing both GPT-4 and WebVoyager setups. |
Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task. |
| Approach: | They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision. |
| Outcome: | The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods . |
On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods. |
| Approach: | They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences. |
| Outcome: | The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance. |
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation (2025.emnlp-main)
Copied to clipboard
Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Existing tree search methods neglect the underlying reasoning process, resulting in poor search quality. |
| Approach: | They propose a framework that systematically explores and refines the reasoning process for code generation by using a tree search engine and a reflection mechanism. |
| Outcome: | The proposed framework outperforms existing methods in the code generation domain. |
CodePRM: Execution Feedback-enhanced Process Reward Model for Code Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in code generation focus on optimizing the thought process, but lack effective process supervision, making it difficult to optimize the thoughts. |
| Approach: | They propose a method that leverages the code execution feedback to build a code PRM by collecting a large dataset of thought traces and then training it to take both the reasoning process and code execution as input. |
| Outcome: | The proposed approach outperforms baselines and strong LLMs in the inference stage. |
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB. |
| Approach: | They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities . |
| Outcome: | The proposed framework can achieve superior performance over the state of the arts. |
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)
Copied to clipboard
Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. |
| Approach: | They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans. |
| Outcome: | The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. |
E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)
Copied to clipboard
Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju
| Challenge: | Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study. |
| Approach: | They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies . |
| Outcome: | The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%. |
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction (2022.findings-acl)
Copied to clipboard
| Challenge: | a Chinese model with whole word masking has no subword because each token is an atomic character. |
| Approach: | They propose to use whole word masking to mask all subwords corresponding to a word at once . they ask models to revise or insert tokens in a masked language modeling manner . |
| Outcome: | The proposed model performs better when one character is inserted or replaced . the model trained with standard character-level masking performs best when one token is masked . |
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers (2024.acl-long)
Copied to clipboard
| Challenge: | Existing transformer-based models struggle with long-sequence processing due to computational costs . a framework to enhance long-content processing of transformers is proposed . |
| Approach: | They propose a framework to enhance long-sequence processing of transformers by three steps . they demonstrate that the framework significantly outperforms prior long-quence processors . |
| Outcome: | The proposed framework outperforms baseline models on long-sequence summarization and reading comprehension tasks. |