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Yansong Feng

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14 papers

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AAAI Conference 2026 Conference Paper

From Single to Societal: Analyzing Persona-Induced Bias in Multi-Agent Interactions

Jiayi Li
Xiao Liu
Yansong Feng

Large Language Model (LLM)-based multi-agent systems are increasingly used to simulate human interactions and solve collaborative tasks. A common practice is to assign agents with personas to encourage behavioral diversity. However, this raises a critical yet underexplored question: do personas introduce biases into multi-agent interactions? This paper presents a systematic investigation into persona-induced biases in multi-agent interactions, with a focus on social traits like trustworthiness (how an agent's opinion is received by others) and insistence (how strongly an agent advocates for its opinion). Through a series of controlled experiments in collaborative problem-solving and persuasion tasks, we reveal that (1) LLM-based agents exhibit biases in both trustworthiness and insistence, with personas from historically advantaged groups (e.g., men and White individuals) perceived as less trustworthy and demonstrating less insistence; and (2) agents exhibit significant in-group favoritism, showing a higher tendency to conform to others who share the same persona. These biases persist across various LLMs, group sizes, and numbers of interaction rounds, highlighting an urgent need for awareness and mitigation to ensure the fairness and reliability of multi-agent systems.

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TCS Journal 2024 Journal Article

Partial prime factor exposure attacks on some RSA variants

Yansong Feng
Abderrahmane Nitaj
Yanbin Pan

In this paper, we consider five variants of the RSA cryptosystem, where the modulus is N = p q, and the public key e and the secret key d satisfy e d − k ( p 2 + p + 1 ) ( q 2 + q + 1 ) = 1 or e d − k ( p 2 − 1 ) ( q 2 − 1 ) = 1. Our results show that if a certain amount of the most significant bits of p are known so that | p − p 0 | = N β with a known p 0, then one can factor the RSA modulus with a better bound than low private exponent attacks. We also present the experimental results to verify our analysis.

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AAAI Conference 2020 Conference Paper

Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

Kun Xu
Linfeng Song
Yansong Feng
Yan Song
Dong Yu

Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding method may not only cause the “many-to-one” problem but also neglect the coordinated nature of this task, that is, each alignment decision may highly correlate to the other decisions. In this paper, we introduce two coordinated reasoning methods, i. e. , the Easy-to-Hard decoding strategy and joint entity alignment algorithm. Speciﬁcally, the Easy-to- Hard strategy ﬁrst retrieves the model-conﬁdent alignments from the predicted results and then incorporates them as additional knowledge to resolve the remaining model-uncertain alignments. To achieve this, we further propose an enhanced alignment model that is built on the current state-of-the-art baseline. In addition, to address the many-to-one problem, we propose to jointly predict entity alignments so that the oneto-one constraint can be naturally incorporated into the alignment prediction. Experimental results show that our model achieves the state-of-the-art performance and our reasoning methods can also signiﬁcantly improve existing baselines.

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IJCAI Conference 2020 Conference Paper

Domain Adaptation for Semantic Parsing

Zechang Li
Yuxuan Lai
Yansong Feng
Dongyan Zhao

Recently, semantic parsing has attracted much attention in the community. Although many neural modeling efforts have greatly improved the performance, it still suffers from the data scarcity issue. In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic parser benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages, i. e. , focusing on domain invariant and domain specific information, respectively. In the coarse stage, our novel domain discrimination component and domain relevance attention encourage the model to learn transferable domain general structures. In the fine stage, the model is guided to concentrate on domain related details. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies. Additionally, we show that our model can well exploit limited target data to capture the difference between the source and target domain, even when the target domain has far fewer training instances.

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AAAI Conference 2020 Conference Paper

Integrating Relation Constraints with Neural Relation Extractors

Yuan Ye
Yansong Feng
Bingfeng Luo
Yuxuan Lai
Dongyan Zhao

Recent years have seen rapid progress in identifying prede- ﬁned relationship between entity pairs using neural networks (NNs). However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often deﬁned over combinations of entity-relation-entity triples, since there often lack of explicitly well-deﬁned type and cardinality requirements for the relations. In this paper, we propose a uniﬁed framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. Particularly, we develop two efﬁcient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction (NRE) models even enhanced with extra post-processing. Our source code and datasets will be released at https: //github. com/PKUYeYuan/ Constraint-Loss-AAAI-2020.

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NeurIPS Conference 2020 Conference Paper

Latent Template Induction with Gumbel-CRFs

Yao Fu
Chuanqi Tan
Bin Bi
Mosha Chen
Yansong Feng
Alexander Rush

Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structures. We explore the use of structured variational autoencoders to infer latent templates for sentence generation using a soft, continuous relaxation in order to utilize reparameterization for training. Specifically, we propose a Gumbel-CRF, a continuous relaxation of the CRF sampling algorithm using a relaxed Forward-Filtering Backward-Sampling (FFBS) approach. As a reparameterized gradient estimator, the Gumbel-CRF gives more stable gradients than score-function based estimators. As a structured inference network, we show that it learns interpretable templates during training, which allows us to control the decoder during testing. We demonstrate the effectiveness of our methods with experiments on data-to-text generation and unsupervised paraphrase generation.

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AAAI Conference 2019 Conference Paper

Lattice CNNs for Matching Based Chinese Question Answering

Yuxuan Lai
Yansong Feng
Xiaohan Yu
Zheng Wang
Kun Xu
Dongyan Zhao

Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to utilize multi-granularity information inherent in the word lattice while maintaining strong ability to deal with the introduced noisy information for matching based question answering in Chinese. We conduct extensive experiments on both document based question answering and knowledge based question answering tasks, and experimental results show that the LCNs models can significantly outperform the state-of-the-art matching models and strong baselines by taking advantages of better ability to distill rich but discriminative information from the word lattice input.

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NeurIPS Conference 2019 Conference Paper

Paraphrase Generation with Latent Bag of Words

Yao Fu
Yansong Feng
John Cunningham

Paraphrase generation is a longstanding important problem in natural language processing. Recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation. We ground the semantics of a discrete latent variable by the target BOW. We use this latent variable to build a fully differentiable content planning and surface realization pipeline. Specifically, we use source words to predict their neighbors and model the target BOW with a mixture of softmax. We use gumbel top-k reparameterization to perform differentiable subset sampling from the predicted BOW distribution. We retrieve the sampled word embeddings and use them to augment the decoder and guide its generation search space. Our latent BOW model not only enhances the decoder, but also exhibits clear interpretability. We show the model interpretability with regard to (1). unsupervised learning of word neighbors (2). the step-by-step generation procedure. Extensive experiments demonstrate the model's transparent and effective generation process.

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IJCAI Conference 2019 Conference Paper

Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

Yuting Wu
Xiao Liu
Yansong Feng
Zheng Wang
Rui Yan
Dongyan Zhao

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

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AIJ Journal 2018 Journal Article

Encoding implicit relation requirements for relation extraction: A joint inference approach

Liwei Chen
Yansong Feng
Songfang Huang
Bingfeng Luo
Dongyan Zhao

Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on. Most existing relation extractors make predictions for each entity pair locally and individually, while ignoring implicit global clues available across different entity pairs and in the knowledge base, which often leads to conflicts among local predictions from different entity pairs. This paper proposes a joint inference framework that employs such global clues to resolve disagreements among local predictions. We exploit two kinds of clues to generate constraints which can capture the implicit type and cardinality requirements of a relation. Those constraints can be examined in either hard style or soft style, both of which can be effectively explored in an integer linear program formulation. Experimental results on both English and Chinese datasets show that our proposed framework can effectively utilize those two categories of global clues and resolve the disagreements among local predictions, thus improve various relation extractors when such clues are applicable to the datasets. Our experiments also indicate that the clues learnt automatically from existing knowledge bases perform comparably to or better than those refined by human.

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IJCAI Conference 2018 Conference Paper

Learning to Converse with Noisy Data: Generation with Calibration

Mingyue Shang
Zhenxin Fu
Nanyun Peng
Yansong Feng
Dongyan Zhao
Rui Yan

The availability of abundant conversational data on the Internet brought prosperity to the generation-based open domain conversation systems. In the training of the generation models, existing methods generally treat all the training data equivalently. However, the data crawled from the websites may contain many noises. Blindly training with the noisy data could harm the performance of the final generation model. In this paper, we propose a generation with calibration framework, that allows high- quality data to have more influences on the generation model and reduces the effect of noisy data. Specifically, for each instance in training set, we employ a calibration network to produce a quality score for it, then the score is used for the weighted update of the generation model parameters. Experiments show that the calibrated model outperforms baseline methods on both automatic evaluation metrics and human annotations.

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AAAI Conference 2018 Conference Paper

Scale Up Event Extraction Learning via Automatic Training Data Generation

Ying Zeng
Yansong Feng
Rong Ma
Zheng Wang
Rui Yan
Chongde Shi
Dongyan Zhao

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables. We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text. We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of highquality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.

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AAAI Conference 2018 Conference Paper

Towards a Neural Conversation Model With Diversity Net Using Determinantal Point Processes

Yiping Song
Rui Yan
Yansong Feng
Yaoyuan Zhang
Dongyan Zhao
Ming Zhang

Typically, neural conversation systems generate replies based on the sequence-to-sequence (seq2seq) model. seq2seq tends to produce safe and universal replies, which suffers from the lack of diversity and information. Determinantal Point Processes (DPPs) is a probabilistic model deﬁned on item sets, which can select the items with good diversity and quality. In this paper, we investigate the diversity issue in two different aspects, namely query-level and system-level diversity. We propose a novel framework which organically combines seq2seq model with Determinantal Point Processes (DPPs). The new framework achieves high quality in generated reply and signiﬁcantly improves the diversity among them. Experiments show that our model achieves the best performance among various baselines in terms of both quality and diversity.

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AAAI Conference 2015 Conference Paper

What Is the Longest River in the USA? Semantic Parsing for Aggregation Questions

Kun Xu
Sheng Zhang
Yansong Feng
Songfang Huang
Dongyan Zhao

Answering natural language questions against structured knowledge bases (KB) has been attracting increasing attention in both IR and NLP communities. The task involves two main challenges: recognizing the questions’ meanings, which are then grounded to a given KB. Targeting simple factoid questions, many existing open domain semantic parsers jointly solve these two subtasks, but are usually expensive in complexity and resources. In this paper, we propose a simple pipeline framework to efficiently answer more complicated questions, especially those implying aggregation operations, e. g. , argmax, argmin. We first develop a transitionbased parsing model to recognize the KB-independent meaning representation of the user’s intention inherent in the question. Secondly, we apply a probabilistic model to map the meaning representation, including those aggregation functions, to a structured query. The experimental results showe that our method can better understand aggregation questions, outperforming the state-of-the-art methods on the Free917 dataset while still maintaining promising performance on a more challenging dataset, WebQuestions, without extra training.

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