Zezhong Wang


2024

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JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialogue Policy Learning
Wai-Chung Kwan | Huimin Wang | Hongru Wang | Zezhong Wang | Bin Liang | Xian Wu | Yefeng Zheng | Kam-Fai Wong
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be. Typically, DPL is cast as a sequential decision problem across a series of predefined action candidates. However, such static and narrow actions can limit response diversity and impede the dialogue agent’s adaptability to new scenarios and edge cases. To overcome these challenges, we introduce a novel Joint Transformer Reinforcement Learning framework, coined as JoTR, where a text-to-text Transformer-based model is employed to directly generate dialogue actions. More concretely, JoTR formulates a token-grained policy, facilitating more dynamic and adaptable dialogue action generation without the need for predefined action candidates. This method not only enhances the diversity of responses but also significantly improves the system’s capability to manage unfamiliar scenarios. Furthermore, JoTR utilizes Reinforcement Learning with a reward-shaping mechanism to efficiently fine-tune the token-grained policy. This allows the model to evolve through interactions, thereby enhancing its performance over time. Our extensive evaluation demonstrates that JoTR surpasses previous state-of-the-art models, showing improvements of 9% and 13% in success rate, and 34% and 37% in the diversity of dialogue actions across two benchmark dialogue modeling tasks respectively. These results have been validated by both user simulators and human evaluators. Code and data are available at ://github.com/KwanWaiChung/JoTR.

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SELF-GUARD: Empower the LLM to Safeguard Itself
Zezhong Wang | Fangkai Yang | Lu Wang | Pu Zhao | Hongru Wang | Liang Chen | Qingwei Lin | Kam-Fai Wong
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

With the increasing risk posed by jailbreak attacks, recent studies have investigated various methods to improve the safety of large language models (LLMs), mainly falling into two strategies: safety training and safeguards. Safety training involves fine-tuning the LLM with adversarial samples, which activate the LLM’s capabilities against jailbreak. However, it is not always effective in countering new attacks and often leads to potential performance degradation. Safeguards, on the other hand, are methods using additional models to filter harmful content from the LLM’s response. Nevertheless, they can only reduce a limited amount of harmful output and introduce extra computational costs. Given the distinct strengths and weaknesses of both, we combine them to balance out their flaws and propose a more effective method called Self-Guard.Specifically, we train the LLM to review its responses for any harmful content and append a [harmful] or [harmless] tag to the end of the response. In this way, Self-Guard possesses the advantages of safety training, leveraging the powerful capabilities of the LLMs themselves to detect harmfulness. Besides that, it gains flexibility like safeguards, making the safety check target the output side, which makes the system less vulnerable to attack updates. Experimental results indicate that our Self-Guard can effectively defend against jailbreak attacks and will not cause LLMs’ performance degradation.

2023

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Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
Liang Chen | Hongru Wang | Yang Deng | Wai Chung Kwan | Zezhong Wang | Kam-Fai Wong
Findings of the Association for Computational Linguistics: ACL 2023

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this popular practice is seriously affected by order sensitivity where different input orders of persona sentences significantly impact the quality and consistency of generated response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and 83.2% on BART). To mitigate the order sensitivity problem, we propose a model-agnostic framework, ORder Insensitive Generation (ORIG), which enables dialogue models to learn robust representation under different persona orders and improve the consistency of response generation. Experiments on the Persona-Chat dataset justify the effectiveness and superiority of our method with two dominant pre-trained models (GPT2 and BART).

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ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing
Zezhong Wang | Luyao Ye | Hongru Wang | Wai-Chung Kwan | David Ho | Kam-Fai Wong
Findings of the Association for Computational Linguistics: EMNLP 2023

Knowledge probing is a task to assess the knowledge encoded within pre-trained language models (PLMs) by having the PLM complete prompts such as “Italy is located in __,”. The model’s prediction precision serves as a lower bound for the amount of knowledge it contains. Subsequent works explore training a series of vectors as prompts to guide PLMs towards more accurate predictions. However, these methods compromise the readability of the prompts. We cannot directly understand these prompts from their literal meaning, making it difficult to verify whether they are correct. Consequently, the credibility of probing results derived from these prompts is diminished. To address the issue, we propose a novel method called ReadPrompt, which aims to identify meaningful sentences to serve as prompts. Experiments show that ReadPrompt achieves state-of-the-art performance on the current knowledge probing benchmark. Moreover, since the prompt is readable, we discovered a misalignment between constructed prompts and knowledge, which is also present in current prompting methods verified by an attack experiment. We claim that the probing outcomes of the current prompting methods are unreliable that overestimate the knowledge contained within PLMs.

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Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Hongru Wang | Rui Wang | Fei Mi | Yang Deng | Zezhong Wang | Bin Liang | Ruifeng Xu | Kam-Fai Wong
Findings of the Association for Computational Linguistics: EMNLP 2023

Large Language Models (LLMs), such as ChatGPT, greatly empower dialogue systems with strong language understanding and generation capabilities. However, most of the previous works prompt the LLMs to directly generate a response based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. Such in-depth dialogue scenarios are challenging for existing LLMs to figure out the user’s hidden needs and respond satisfactorily through a single-step inference. To this end, we propose a novel linguistic cue-based chain-of-thoughts (Cue-CoT), which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response. To evaluate the approach, we build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues during the conversation: personality, emotion, and psychology. We conducted experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shot settings. Empirical results demonstrate our proposed Cue-CoT method outperforms standard prompting methods in terms of both helpfulness and acceptability on all datasets.

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MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging
Hongru Wang | Zezhong Wang | Wai Chung Kwan | Kam-Fai Wong
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Fangkai Yang | Pu Zhao | Zezhong Wang | Lu Wang | Bo Qiao | Jue Zhang | Mohit Garg | Qingwei Lin | Saravan Rajmohan | Dongmei Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, an area not extensively covered in general LLMs, making it well-suited for evaluating methods aiming to enhance LLMs’ domain-specific capabilities. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our method outperforms the commonly used LLM with retrieval methods. We make our source code and sample data available at: https://aka.ms/Microsoft_QA.

2022

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Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning
Zezhong Wang | Hongru Wang | Wai Chung Kwan | Kam-Fai Wong
Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022)

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I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese
Wenbo Shang | Jiangjiang Zhao | Zezhong Wang | Binyang Li | Fangchun Yang | Kam-Fai Wong
Findings of the Association for Computational Linguistics: EMNLP 2022

Humor plays an important role in our daily life, as it is an essential and fascinating element in the communication between persons. Therefore, how to recognize punchlines from the dialogue, i.e. conversational humor recognition, has attracted much interest of computational linguistics communities. However, most existing work attempted to understand the conversational humor by analyzing the contextual information of the dialogue, but neglected the character of the interlocutor, such as age, gender, occupation, and so on. For instance, the same utterance could bring out humorous from a serious person, but may be a plain expression from a naive person. To this end, this paper proposes a Character Fusion Conversational Humor Recognition model (CFCHR) to explore character information to recognize conversational humor. CFCHR utilizes a multi-task learning framework that unifies two highly pertinent tasks, i.e., character extraction and punchline identification. Based on deep neural networks, we trained both tasks jointly by sharing weight to extract the common and task-invariant features while each task could still learn its task-specific features. Experiments were conducted on Chinese sitcoms corpus, which consisted of 12,677 utterances from 22 characters. The experimental results demonstrated that CFCHR could achieve 33.08% improvements in terms of F1-score over some strong baselines, and proved the effectiveness of the character information to identify the punchlines.