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Haofen Wang

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12 papers

1 author row

AAAI Conference 2026 Conference Paper

Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction

Jun Xu
Xinkai Du
Yu Ao
Peilong Zhao
Yang Li
Ling Zhong
Lin Yuan
Zhongpu Bo

Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approaches neglect supervision over the reasoning process, making it difficult to guarantee logical coherence and rigor. To address these limitations, we propose Thinker, a hierarchical thinking model for deep search through multi-turn interaction, making the reasoning process supervisable and verifiable. It decomposes complex problems into independently solvable sub-problems, each dually represented in both natural language and an equivalent logical function to support knowledge base and web searches. Concurrently, dependencies between sub-problems are passed as parameters via these logical functions, enhancing the logical coherence of the problem-solving process. To avoid unnecessary external searches, we perform knowledge boundary determination to check if a sub-problem is within the LLM's intrinsic knowledge, allowing it to answer directly. Experimental results indicate that with as few as several hundred training samples, the performance of Thinker is competitive with established baselines. Furthermore, when scaled to the full training set, Thinker significantly outperforms these methods across various datasets and model sizes.

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AAAI Conference 2025 Conference Paper

Cognitive Bias and Reassignment: Who Can Contribute High Quality LLM Data

Yunfan Gao
Yun Xiong
Zhongyuan Hu
Yiming Zhang
Meng Wang
Haofen Wang

In recent years, the rapid development of Large Language Models has highlighted the urgent need for large-scale, high-quality, and diverse data. We have launched an LLM data co-creation platform aimed at bringing together a wide range of participants to contribute data. Within six months, the platform has attracted over 10,000 participants who contributed more than 150,000 data entries across more than 200 tasks. An observable user cohort was constructed around the question, "Who is the best data contributor?" along with sub-questions concerning user preferences, task competence, and more. Through a detailed analysis of data contributors, this paper reveals several data collection patterns related to human factors. It reveals that contributors who provide high-quality data often do not meet initial expectations, as their behavior exhibits typical characteristics of the Dunning-Kruger effect. This paper examined the cognitive bias between users' self-assessment and actual abilities, where individuals tend to overestimate their capabilities in certain tasks, leading to a decreased willingness to continue contributing and a consequent waste of human resources. To address this issue, we propose a task reassignment method based on multi-task fine-tuning of small language models (SLMs) to better align user groups with appropriate task types. After the reallocation, we observed a significant increase in user engagement and platform benefits, along with improved overall platform efficiency. The versatility of this method makes it applicable to broader data collection scenarios.

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NeurIPS Conference 2025 Conference Paper

ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Mingyang Chen
Linzhuang Sun
Tianpeng Li
Haoze Sun
Chenzheng Zhu
Haofen Wang
Jeff Pan
Wen Zhang

Large Language Models (LLMs) have shown remarkable capabilities in reasoning, exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating reasoning with external search processes remains challenging, especially for complex multi-hop questions requiring multiple retrieval steps. We propose ReSearch, a novel framework that trains LLMs to Reason with Search via reinforcement learning without using any supervised data on reasoning steps. Our approach treats search operations as integral components of the reasoning chain, where when and how to perform searches is guided by text-based thinking, and search results subsequently influence further reasoning. We train ReSearch on Qwen2. 5-7B(-Instruct) and Qwen2. 5-32B(-Instruct) models and conduct extensive experiments. Despite being trained on only one dataset, our models demonstrate strong generalizability across various benchmarks. Analysis reveals that ReSearch naturally elicits advanced reasoning capabilities such as reflection and self-correction during the reinforcement learning process.

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NeurIPS Conference 2025 Conference Paper

Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning

Tianxing Wu
Shutong Zhu
Jingting Wang
Ning Xu
Guilin Qi
Haofen Wang

Uncertain knowledge graphs (UKGs) associate each triple with a confidence score to provide more precise knowledge representations. Recently, since real-world UKGs suffer from the incompleteness, uncertain knowledge graph (UKG) completion attracts more attention, aiming to complete missing triples and confidences. Current studies attempt to learn UKG embeddings to solve this problem, but they neglect the extremely imbalanced distributions of triple confidences. This causes that the learnt embeddings are insufficient to high-quality UKG completion. Thus, in this paper, to address the above issue, we propose a new semi-supervised Confidence Distribution Learning (ssCDL) method for UKG completion, where each triple confidence is transformed into a confidence distribution to introduce more supervision information of different confidences to reinforce the embedding learning process. ssCDL iteratively learns UKG embedding by relational learning on labeled data (i. e. , existing triples with confidences) and unlabeled data with pseudo labels (i. e. , unseen triples with the generated confidences), which are predicted by meta-learning to augment the training data and rebalance the distribution of triple confidences. Experiments on two UKG datasets demonstrate that ssCDL consistently outperforms the state-of-the-art baselines in different evaluation metrics.

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AAAI Conference 2024 Conference Paper

A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis

Nailei Hei
Qianyu Guo
Zihao Wang
Yan Wang
Haofen Wang
Wenqiang Zhang

Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-level guidance, it is challenging for novice users to achieve the desired results by manually entering prompts due to a discrepancy between novice-user-input prompts and the model-preferred prompts. To bridge the distribution gap between user input behavior and model training datasets, we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG) for automated prompt optimization. For CFP, we construct a novel dataset for text-to-image tasks that combines coarse and fine-grained prompts to facilitate the development of automated prompt generation methods. For UF-FGTG, we propose a novel framework that automatically translates user-input prompts into model-preferred prompts. Specifically, we propose a prompt refiner that continually rewrites prompts to empower users to select results that align with their unique needs. Meanwhile, we integrate image-related loss functions from the text-to-image model into the training process of text generation to generate model-preferred prompts. Additionally, we propose an adaptive feature extraction module to ensure diversity in the generated results. Experiments demonstrate that our approach is capable of generating more visually appealing and diverse images than previous state-of-the-art methods, achieving an average improvement of 5% across six quality and aesthetic metrics. Data and code are available at https://github.com/Naylenv/UF-FGTG.

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IJCAI Conference 2024 Conference Paper

Towards Proactive Interactions for In-Vehicle Conversational Assistants Utilizing Large Language Models

Huifang Du
Xuejing Feng
Jun Ma
Meng Wang
Shiyu Tao
YiJie Zhong
Yuan-Fang Li
Haofen Wang

Research demonstrates that the proactivity of in-vehicle conversational assistants (IVCAs) can help to reduce distractions and enhance driving safety, better meeting users' cognitive needs. However, existing IVCAs struggle with user intent recognition and context awareness, which leads to suboptimal proactive interactions. Large language models (LLMs) have shown potential for generalizing to various tasks with prompts, but their application in IVCAs and exploration of proactive interaction remain under-explored. These raise questions about how LLMs improve proactive interactions for IVCAs and influence user perception. To investigate these questions systematically, we establish a framework with five proactivity levels across two dimensions—assumption and autonomy—for IVCAs. According to the framework, we propose a ``Rewrite + ReAct + Reflect'' strategy, aiming to empower LLMs to fulfill the specific demands of each proactivity level when interacting with users. Both feasibility and subjective experiments are conducted. The LLM outperforms the state-of-the-art model in success rate and achieves satisfactory results for each proactivity level. Subjective experiments with 40 participants validate the effectiveness of our framework and show the proactive level with strong assumptions and user confirmation is most appropriate.

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IJCAI Conference 2022 Conference Paper

Position-aware Joint Entity and Relation Extraction with Attention Mechanism

Chenglong Zhang
Shuyong Gao
Haofen Wang
Wenqiang Zhang

Named entity recognition and relation extraction are two important core subtasks of information extraction, which aim to identify named entities and extract relations between them. In recent years, span representation methods have received a lot of attention and are widely used to extract entities and corresponding relations from plain texts. Most recent works focus on how to obtain better span representations from pre-trained encoders, but ignore the negative impact of a large number of span candidates on slowing down the model performance. In our work, we propose a joint entity and relation extraction model with an attention mechanism and position-attentive markers. The attention score of each candidate span is calculated, and most of the candidate spans with low attention scores are pruned before being fed into the span classifier, thus achieving the goal of removing the most irrelevant spans. At the same time, in order to explore whether the position information can improve the performance of the model, we add position-attentive markers to the model. The experimental results show that our model is effective. With the same pre-trained encoder, our model achieves the new state-of-the-art on standard benchmarks (ACE05, CoNLL04 and SciERC), obtaining a 4. 7%-17. 8% absolute improvement in relation F1.

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AAAI Conference 2018 Conference Paper

Adversarial Learning for Chinese NER From Crowd Annotations

YaoSheng Yang
Meishan Zhang
Wenliang Chen
Wei Zhang
Haofen Wang
Min Zhang

To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. In this paper, we propose an approach to performing crowd annotation learning for Chinese Named Entity Recognition (NER) to make full use of the noisy sequence labels from multiple annotators. Inspired by adversarial learning, our approach uses a common Bi-LSTM and a private Bi-LSTM for representing annotatorgeneric and -speciﬁc information. The annotator-generic information is the common knowledge for entities easily mastered by the crowd. Finally, we build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we create two data sets for Chinese NER tasks from two domains. The experimental results show that our system achieves better scores than strong baseline systems.

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AAAI Conference 2018 Conference Paper

Personalized Time-Aware Tag Recommendation

Keqiang Wang
Yuanyuan Jin
Haofen Wang
Hongwei Peng
Xiaoling Wang

Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensor factorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which can not ﬁnd users’ potential interests. In this paper, we propose an uniﬁed tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weights to user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative ﬁltering (CF) to timeaware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time-aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efﬁciency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

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AAAI Conference 2016 Conference Paper

Cross-Lingual Taxonomy Alignment with Bilingual Biterm Topic Model

Tianxing Wu
Guilin Qi
Haofen Wang
Kang Xu
Xuan Cui

As more and more multilingual knowledge becomes available on the Web, knowledge sharing across languages has become an important task to beneﬁt many applications. One of the most crucial kinds of knowledge on the Web is taxonomy, which is used to organize and classify the Web data. To facilitate knowledge sharing across languages, we need to deal with the problem of cross-lingual taxonomy alignment, which discovers the most relevant category in the target taxonomy of one language for each category in the source taxonomy of another language. Current approaches for aligning crosslingual taxonomies strongly rely on domain-speciﬁc information and the features based on string similarities. In this paper, we present a new approach to deal with the problem of cross-lingual taxonomy alignment without using any domain-speciﬁc information. We ﬁrst identify the candidate matched categories in the target taxonomy for each category in the source taxonomy using the crosslingual string similarity. We then propose a novel bilingual topic model, called Bilingual Biterm Topic Model (BiBTM), to perform exact matching. BiBTM is trained by the textual contexts extracted from the Web. We conduct experiments on two kinds of real world datasets. The experimental results show that our approach signiﬁcantly outperforms the designed state-of-the-art comparison methods.

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AAAI Conference 2012 Conference Paper

A New Operator for ABox Revision in DL-Lite

Sibei Gao
Guilin Qi
Haofen Wang

In this paper, we propose a new operator for revising ABoxes in DL-Lite ontologies. We present a graphbased algorithm for ABox revision in DL-Lite, which implements the revision operator and we show it runs in polynomial time

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TIST Journal 2012 Journal Article

EachWiki

Haofen Wang
Linyun Fu
Wei Jin
Yong Yu

Wikipedia, one of the best-known wikis and the world’s largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.

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