Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framework, CANOE, to reduce faithfulness hallucinations of LLMs across different downstream tasks without human annotations. Specifically, we first synthesize short-form question-answering (QA) data with four diverse tasks to construct high-quality and easily verifiable training data without human annotation. Also, we propose Dual-GRPO, a rule-based reinforcement learning method that includes three tailored rule-based rewards derived from synthesized short-form QA data, while simultaneously optimizing both short-form and long-form response generation. Notably, Dual-GRPO eliminates the need to manually label preference data to train reward models and avoids over-optimizing short-form generation when relying only on the synthesized short-form QA data. Experimental results show that CANOE greatly improves the faithfulness of LLMs across 11 different tasks, even outperforming the most advanced LLMs, e.g., GPT-4o and OpenAI o1.

Shuzheng Si, Haozhe Zhao, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Shuzheng Si, Haozhe Zhao, Gang Chen, Yunshui Li, Kangyang Luo, Chuancheng Lv, Kaikai An, Fanchao Qi, Baobao Chang, Maosong Sun. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Abstract Tokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., at the sub-character level. To utilize such information, we propose sub-character (SubChar for short) tokenization. Specifically, we first encode the input text by converting each Chinese character into a short sequence based on its glyph or pronunciation, and then construct the vocabulary based on the encoded text with sub-word segmentation. Experimental results show that SubChar tokenizers have two main advantages over existing tokenizers: 1) They can tokenize inputs into much shorter sequences, thus improving the computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos. At the same time, models trained with SubChar tokenizers perform competitively on downstream tasks. We release our code and models at https://github.com/thunlp/SubCharTokenization to facilitate future work.

It is very common to use quotations (quotes) to make our writings more elegant or convincing.To help people find appropriate quotes efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing.There have been various quote recommendation approaches, but they are evaluated on different unpublished datasets.To facilitate the research on this task, we build a large and fully open quote recommendation dataset called QuoteR, which comprises three parts including English, standard Chinese and classical Chinese.Any part of it is larger than previous unpublished counterparts.We conduct an extensive evaluation of existing quote recommendation methods on QuoteR.Furthermore, we propose a new quote recommendation model that significantly outperforms previous methods on all three parts of QuoteR.All the code and data of this paper can be obtained at https://github.com/ thunlp/QuoteR.

Biru Zhu, Yujia Qin, Fanchao Qi, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation.However, most work mixes all these roles, obscuring the problem definitions and research goals of the security role that aims to reveal the practical concerns of NLP models.In this paper, we rethink the research paradigm of textual adversarial samples in security scenarios.We discuss the deficiencies in previous work and propose our suggestions that the research on the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their methods on security tasks to demonstrate the real-world concerns; (2) consider realworld attackers' goals, instead of developing impractical methods.To this end, we first collect, process, and release a security datasets collection Advbench.Then, we reformalize the task and adjust the emphasis on different goals in SoadNLP.Next, we propose a simple method based on heuristic rules that can easily fulfill the actual adversarial goals to simulate real-world attack methods.We conduct experiments on both the attack and the defense sides on Advbench.Experimental results show that our method has higher practical value, indicating that the research paradigm in SoadNLP may start from our new benchmark.All the code and data of Advbench can be obtained at https: //github.com/thunlp/Advbench.

Backdoor attacks are a kind of emergent security threat in deep learning.After being injected with a backdoor, a deep neural model will behave normally on standard inputs but give adversary-specified predictions once the input contains specific backdoor triggers.In this paper, we find two simple tricks that can make existing textual backdoor attacks much more harmful.The first trick is to add an extra training task to distinguish poisoned and clean data during the training of the victim model, and the second one is to use all the clean training data rather than remove the original clean data corresponding to the poisoned data.These two tricks are universally applicable to different attack models.We conduct experiments in three tough situations including clean data fine-tuning, low-poisoningrate, and label-consistent attacks.Experimental results show that the two tricks can significantly improve attack performance.This paper exhibits the great potential harmfulness of backdoor attacks.All the code and data can be obtained at https://github.com/ thunlp/StyleAttack.

Review generation, aiming to automatically generate review text according to the given information, is proposed to assist in the unappealing review writing. However, most of existing methods only consider the overall sentiments of reviews and cannot achieve aspect-level sentiment control. Even though some previous studies attempt to generate aspect-level sentiment-controllable reviews, they usually require large-scale human annotations which are unavailable in the real world. To address this issue, we propose a mutual learning framework to take advantage of unlabeled data to assist the aspect-level sentiment-controllable review generation. The framework consists of a generator and a classifier which utilize confidence mechanism and reconstruction reward to enhance each other. Experimental results show our model can achieve aspect-sentiment control accuracy up to 88% without losing generation quality.

Fanchao Qi, Mukai Li, Yangyi Chen, Zhengyan Zhang, Zhiyuan Liu, Yasheng Wang, Maosong Sun. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Fanchao Qi, Yuan Yao, Sophia Xu, Zhiyuan Liu, Maosong Sun. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Backdoor attacks are a kind of emergent training-time threat to deep neural networks (DNNs).They can manipulate the output of DNNs and possess high insidiousness.In the field of natural language processing, some attack methods have been proposed and achieve very high attack success rates on multiple popular models.Nevertheless, there are few studies on defending against textual backdoor attacks.In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and, to the best of our knowledge, is the first method that can handle all the textual backdoor attack situations.Experiments demonstrate the effectiveness of our model in defending BiLSTM and BERT against five different backdoor attacks.All the code and data of this paper can be obtained at https: //github.com/thunlp/ONION.

Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning.Both of them harness taskirrelevant features of data in their implementation.Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks.In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning.We design an adversarial attack method and a backdoor attack method, and conduct extensive experiments to evaluate them.Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer-the attack success rates can exceed 90% without much effort.It reflects the limited ability of NLP models to handle the feature of text style that has not been widely realized.In addition, the style transfer-based adversarial and backdoor attack methods show superiority to baselines in many aspects.All the code and data of this paper can be obtained at https:// github.com/thunlp/StyleAttack.

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this article, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This article provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.

A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain words annotated with sememes, have been successfully applied to many NLP tasks. However, existing sememe KBs are built on only a few languages, which hinders their widespread utilization. To address the issue, we propose to build a unified sememe KB for multiple languages based on BabelNet, a multilingual encyclopedic dictionary. We first build a dataset serving as the seed of the multilingual sememe KB. It manually annotates sememes for over 15 thousand synsets (the entries of BabelNet). Then, we present a novel task of automatic sememe prediction for synsets, aiming to expand the seed dataset into a usable KB. We also propose two simple and effective models, which exploit different information of synsets. Finally, we conduct quantitative and qualitative analyses to explore important factors and difficulties in the task. All the source code and data of this work can be obtained on https://github.com/thunlp/BabelNet-Sememe-Prediction.

A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. Our model comprises a sentence encoder and multiple predictors. The predictors are expected to identify different characteristics of the target word from the input query. We evaluate our model on English and Chinese datasets including both dictionary definitions and human-written descriptions. Experimental results show that our model achieves the state-of-the-art performance, and even outperforms the most popular commercial reverse dictionary system on the human-written description dataset. We also conduct quantitative analyses and a case study to demonstrate the effectiveness and robustness of our model. All the code and data of this work can be obtained on https://github.com/thunlp/MultiRD.

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks.Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input.Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods.However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed.In this paper, we propose a novel attack model, which incorporates the sememebased word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately.We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets.Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods.Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training.

Word sense disambiguation (WSD) is a fundamental natural language processing task.Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory and has wider practical use than supervised WSD that requires a mass of sense-annotated data.HowNet is the most widely used lexical knowledge base in Chinese WSD.Because of its uniqueness, however, most of existing unsupervised WSD methods cannot work for HowNetbased WSD, and the tailor-made methods have not obtained satisfying results.In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models.In experiments, considering existing evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset.Experimental results demonstrate that our model achieves significantly better performance than all the baseline methods.All the code and data of this paper are available at https://github.com/thunlp/SememeWSD.

Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit sememes in specific tasks and few efforts are made to utilize sememes more fundamentally. In this paper, we propose to incorporate sememes into recurrent neural networks (RNNs) to improve their sequence modeling ability, which is beneficial to all kinds of downstream tasks. We design three different sememe incorporation methods and employ them in typical RNNs including LSTM, GRU and their bidirectional variants. In evaluation, we use several benchmark datasets involving PTB and WikiText-2 for language modeling, SNLI for natural language inference and another two datasets for sentiment analysis and paraphrase detection. Experimental results show evident and consistent improvement of our sememe-incorporated models compared with vanilla RNNs, which proves the effectiveness of our sememe incorporation methods. Moreover, we find the sememe-incorporated models have higher robustness and outperform adversarial training in defending adversarial attack. All the code and data of this work can be obtained at https://github.com/thunlp/SememeRNN.

Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents.Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models.In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment.Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememeincorporated models in learning representations of multiword expressions, a typical task of SC.In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations.Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge.We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC.All the code and data of this paper can be obtained on https: //github.com/thunlp/Sememe-SC.

Sememes are defined as the minimum semantic units of human languages.As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks.However, most languages still do not have sememe-based linguistic knowledge bases.Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages.We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction.Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction.The codes and data of this paper are available at https: //github.com/thunlp/CL-SP.