Towards Fewer Hallucinations in Knowledge-Grounded Dialogue Generation via Augmentative and Contrastive Knowledge-Dialogue
Year: 2023

Abstract:
Existing knowledge-grounded open-domain dialogue generation models often face the hallucination problem, i.e. the dialogue generative model will persist in an inappropriate knowledge and generate responses that inconsistent with the facts. We argue that this problem mainly stems from the polarized optimization objectives and weak knowledge generation ability. To mitigate the hallucination, we take inspiration from human communicating that people will replay euphemistic responses for the unclear or unrecognizable knowledge, and propose an Augmentative and Contrastive Knowledge Dialogue Expansion Framework (ACKDEF). ACK-DEF constructs the augmentative and contrastive knowledge dialogue samples, which consist of the knowledge of different degrees of errors and the response of manual design, to expand the original training set and smooth the polarized optimization objective that enables models to generate ground-truth with or without gold knowledge. Not only the knowledge, ACK-DEF also provides the tactful responses of manual design corresponding to the incomplete correct knowledge. Experimental results on the Wikipedia of Wizard dataset show that employing the ACK-DEF is effective to alleviate the hallucination problem.

heading: 1 Introduction
text: Recently, Knowledge-Grounded Dialogue Generation draws dramatic attentions in artificial intelligence community. Many efforts incorporate knowledge information to improve the performance of dialogue generation models (Zhou et al., 2018; Dinan et al., 2019; Gopalakrishnan et al., 2019; Kim et al., 2020; Zhao et al., 2020a; Zheng et al., 2021; Zhao et al., 2022a; Bao et al., 2022). However, these methods always face the hallucination problem, that is, the dialogue generation model may insist on an inappropriate knowledge and generate responses that inconsistent with the facts (Rashkin et al., 2021; Zhao et al., 2022a; Dziri et al., 2022).
We argue that the hallucination problem primarily caused by two aspects: (1) The optimization objective is usually polarized by the gold knowledgedialogue samples and general dialogue samples without knowledge in current knowledge-grounded dialogue datasets (Zhou et al., 2018; Gopalakrishnan et al., 2019; Dinan et al., 2019; Wu et al., 2019; Komeili et al., 2022). Few datasets consider teaching models how to respond when dealing with incomplete correct knowledge, which makes the models tend to believe in the given knowledge, regardless of whether the knowledge is appropriate or not, resulting in hallucination problems. In addition, the knowledge retrieval system tends to extract irrelevant knowledge rather than relevant knowledge when the database is large, aggravating the hallucinations (Reimers and Gurevych, 2021; Liu et al., 2022). (2) The generation of knowledge may also face the hallucination problem and obtain the inappropriate knowledge, leading the generation of hallucination responses (Kim et al., 2020; Zhao et al., 2020a; Liu et al., 2022; Adolphs et al., 2021; Bao et al., 2022).
To mitigate the hallucination problem, we propose an Augmentative and Contrastive Knowledge Dialogue Expansion Framework (ACK-DEF), which is inspirited by human communicating that people will replay euphemistic response for the unrecognizable knowledge. ACK-DEF is proposed to smooth the polarized optimization objective by augmenting training set with augmentative and contrastive knowledge-dialogue samples. Not only the knowledge, we also designed the reply patterns for the knowledge with different level of errors. For this, we propose the augmentative knowledge dialogue expansion (AK), and contrastive knowledge dialogue expansion (CK). AK is proposed to boost the generalization ability of models on knowledge with minor noise. On the contrary, inspired from the contrastive learning paradigm (Cai et al., 2020; Chen et al., 2020a,b; Sun et al., 2021, 2022), CK
1741
Knowledge
Query
Response
Noise Knowledge
Query
Pseudo Response
Antonym
Replace
Contrastive Knowledge dialogue expansion
Sample Candidate
Knowledge
Query
Response
Less-Noise Knowledge
Query
Synonym
Replace
Augmentative Knowledge dialogue expansion
More-Noise Knowledge
Query
Prefix+ Response+Post
Response
Concatenate
Prefix Post
ak-less sample
ak-more sample
ck sample
Figure 1: A diagram of our Augmentative Knowledge dialogue expansion method. We replace different proportion of words in the original knowledge with synonyms to construct incomplete correct knowledge, and design response for different knowledge. We also use prompts to guide the dialogue generation process.
reconstructs incorrect knowledge and designs euphemistic responses, which aims to push the model learn the reply pattern of incorrect knowledge and a better boundary between correct and incorrect knowledge.
Contributions: We propose an ACK-DEF to construct new knowledge-dialogue samples that consist of knowledge with different level of errors and manual responses, to soften the training optimization objectives of models, which will mitigate the hallucination. Finally, we conduct extension experiments to show the superior performance of ACK-DEF on alleviating the hallucination.

heading: 2 Methodology
text: To mitigate the hallucination problem that caused by the polarized optimization objectives in knowledge grounded dialogue generation, we take inspiration from human communicating, and propose the Augmentative and Contrastive Knowledge Dialogue Expansion Framework (ACK-DEF). Our ACK-DEF aims to soften the polarized training optimization objectives of current knowledgegrounded dialogue generation methods, and guide the dialogue system reply patterns for the knowledge with different level of errors. To achieve this end, we design two effective expansion method, which will be detailed in below.

heading: 2.1 Augmentative Knowledge Dialogue
text: We propose the Augmentative Knowledge (AK) dialogue expansion to boost the generalization ability of the dialogue model on the knowledge with simi-
Augmentative Knowledge dialogue expansion
Prefix+ Response+Post
Prefix Post
ak-more sample
lar semantics but different expressions, which can prevent the model from being interfered by partialrelevant knowledge retrieved by the retrieval systems (Lian et al., 2019; Zhao et al., 2020b; Hedayatnia et al., 2020; Zheng et al., 2021; Shuster et al., 2021; Komeili et al., 2022). As shown in Figure 1, we employ the synonym data augmentation tool, which replaces words in the original knowledge with synonyms, to reconstruct the knowledge information (Miller, 1995). Considering that the synonym may disrupt the original semantics of new constructed knowledge, we constrain the replace possibility within [0.1,0.2]. Hence, we can obtain the approximate knowledge. Combining this knowledge and the original dialogue, we obtain the “ak-less sample”. In addition, we also replace 30% to 50% words with their synonyms to construct the less similar knowledge. Inspired from prompt learning paradigm (Yao et al., 2022; Valvoda et al., 2022; Zhao et al., 2022b), we manually produce some Prefix-prompts and Post-prompts (see Appendix) to (1) make the new response more tactful for the less similar knowledge; (2) regulate and guide the dialogue generation process of the model. We call the sample consist of less-similar knowledge and designed response as “ak-more sample”.

heading: 2.2 Contrastive Knowledge Dialogue
text: We propose the Contrastive Knowledge (CK) dialogue expansion, inspired from the contrastive learning paradigm (Chen et al., 2020b; Cai et al., 2020), not only construct the incorrect knowledge as negative samples for original knowledge, but also build the euphemistic responses as positive
samples for the original response with incorrect knowledge.1 To help the model learn a boundary between correct and incorrect knowledge, we employ the antonym to make up new incorrect knowledge. For example, given the knowledge “nintendo was founded on 23 september 1889 ...”, the “founded” will be replaced with “abolish”, which greatly changes the semantics but little changes the expression. After that, we random choose an euphemistic response to replace the original response of the dialogue. Finally, The incorrect knowledge and the replaced euphemistic response are combined as the “ck-sample”.

heading: 3 Experiment and Results

heading: 3.1 Experiment Settings

heading: 3.1.1 Dataset
text: We use the Wikipedia of Wizard (WoW) data, a well-established knowledge-grounded opendomain dialogue dataset, for our experiment. We pre-precess the WoW dataset and extract the singleturn knowledge dialogue samples. To evaluate the performance of our method in detail, we perform four test sets: normal, ak-less, ak-more and ck. The normal set is the original test set. And the ak-less, ak-more and ck are the sets consist of ak-less, ak-more and ck samples, respectively. We also follow the settings of WoW data and divide the test set into two groups (seen test and unseen test): the topic of the knowledge in the unseen test set is missing in the training set.

heading: 3.1.2 Baseline
text: We employ the released PLATO-v1 (Bao et al., 2020) model, a pre-trained dialogue generation model based on UniLM, for our experiment.
Fine-tuning We directly finetune a model on the original WoW training set. By this, the model can only see gold knowledge dialogue samples and general dialogue samples without knowledge. Hence, we call the fine-tuned model PLATO+GOLD.
Fine-tuning with ACK-DEF We finetune the model with the original set and the expansion samples that obtained through ACK-DEF. Thence, we call it PLATO+ACK-DEF.
1We manually construct some responses, please see Appendix for the detail.

heading: 3.1.3 AutoEvaluation Metrics
text: Dialogue Metrics Our primary metrics of interest are Distinct-n (Li et al., 2016), Response Length (Len.) (Csaky et al., 2019), BLEU (Papineni et al., 2002), Embedding-based (Greedy (GRE), Average (AVG), Extrema (EXT)) (Liu et al., 2016), and Coherence (COH) (Xu et al., 2018). Distinct-n evaluates the diversity of generated responses, which is calculated through the ratio of distinct n-grams and all generated n-grams. Len. is the average number of words of all generated responses. BLEU validates the degree of the word-overlap between the generated response and the ground-truth, which denotes the consistence between generated response and ground-truth. Embedding-based metrics (GRE, AVG and EXT) are introduced to evaluate the semantic relationship of generated responses and ground-truth responses, illustrating the consistence in semantic level. COH. mainly assesses the relevance between contexts and generated responses.
Knowledge Metrics We follow the PLATO(Bao et al., 2020) and use the knowledge precision, recall and f1 scores. These metrics are used to calculate the ratio of tokens that exist in common in groundtruth knowledge and generated responses to tokens in generated responses. “Recall” is the average ratio of the number of overlapping tokens in response and knowledge to the number of tokens in knowledge. And “Precision” is the average ratio of the number of overlapping tokens to the number of tokens in response. In other words, “Recall” indicates how much knowledge information is contained in the response, while “Precision” indicates the proportion of knowledge information in the response. Even we involve the negative and incorrect knowledge in response generation, we still use the ground-truth knowledge to calculate the metrics in
Table 3,4.

heading: 3.2 Dialogue Performance Analysis
text: Table 1 and Table 2 report the automatic results on four test sets and four unseen test sets, respectively. In these Tables, it can be observed that (1) the PLATO+ACK-DEF has a competitive performance with PLATO+GOLD on the normal set, which means that the PLATO+ACK-DEF can recognize the golden knowledge and produce appropriate responses. (2) the PLATO+GOLD perform worse than PLATO+ACK-DEF on ak-less, which means that the robustness of the dialogue model only trained with golden knowledge is very weak.
Even if the knowledge information only changes by 10% to 20%, the performance of the model will
significantly decline, especially consistency metrics (i.e. BLEU, GRE, AVG and EXT). (3) the PLATO+GOLD achieve better Distinct scores but weaker BLEU and embedding-based scores, which means that the PLATO+GOLD is easy to generate responses that are very different from ground-truth responses, that is, the hallucinations.

heading: 3.3 Knowledge Correlation Analysis
text: Table 3 and Table 4 report the knowledge correlation result of PLATO+GOLD and PLATO+ACKDEF on four test sets and four test unseen sets, respectively. From these table, we can observe that the performance of PLATO+GOLD is reduced when the given knowledge changed, which illustrates the danger that the model generate responses based on incorrect knowledge. In addition to the above findings, we also observed that the recall, precision and f1 scores of PLATO+ACK-DEF are better than PLATO+GOLD on ak-less and ak-more sets, which demonstrates that using ACK-DEF effectively enhance the model’s capability for the similar knowledge information. Moreover, the result of PLATO+ACK-DEF on the ck set is significantly reduced, which shows that the model distinguishes the wrong knowledge constructed with antonyms and gives an appropriate response with-
out knowledge (see Table 1 and Table 2 for the effect). These results are inline with our exception that incorporating noised knowledge dialogue samples in training stages can smooth the polarized optimization objective, and mitigate the hallucination problem.
According to the results of test seen sets and unseen sets), we can notice that the PLATO+ACKDEF achieves a good performance on groundtruth seen knowledge and a weak performance on ground-truth unseen knowledge. This illustrates that the PLATO+ACK-DEF may doubt the authenticity of unseen given knowledge (even if the knowledge is the ground-truth), and will not fully use it to generate responses. This may alleviate the hallucination, and we believe it is caused by (1) the Augmentative knowledge dialogue introduce similar knowledge to improve the generalization of the model; (2) the Contrastive knowledge dialogue introduce knowledge independent responses, which tell the model to generate responses without knowledge; (3) the ACK-DEF smooths the polarized optimization, which ensure the model not to directly use the given knowledge.

heading: 3.4 Human Evaluation
text: To further evaluation the ability of our ACK-DEF on reducing the hallucination problem, we randomly select 400 samples form four test sets, and hire three annotators to do human evaluations by assessing whether the responses generated by PLATO +GOLD and +ACK-DEL have hallucinations. Table 5 reports the results of human evaluation, from which we can notice that the PLATO+ACK-DEF generate less hallucinations than PLATO+GOLD. This shows the effectiveness of our ACK-DEF.

heading: 3.5 Case Study
text: Table 6 shows a generated case of PLATO+GOLD (GOLD) and PLATO+ACK-DEF (ACK-DEF) based on different knowledge information. We can observed that the GOLD is convinced of the given knowledge, regardless of whether the knowledge
is appropriate or not, and more easily to copy the knowledge information into responses. Even the GOLD has seen the knowledge topic, it could not remember the knowledge in their parameters. On the contrary, the ACK-DEF has good resistance to incomplete correct knowledge.

heading: 4 Conclusion
text: This paper focuses on the hallucinations caused by polarized optimization objective in knowledgegrounded dialogue generation (KGDG), and proposes an augmentative and contrastive knowledge dialogue expansion framework (ACK-DEF) to mitigate it. The optimization objective of KGDG is to train the model could generate proper response with or without knowledge, which inevitably weaken the model’s ability on unrecognized knowledge and lead hallucinations. Therefore, ACK-DEF constructs multiple level knowledge-dialogue samples to soften the optimization objective of KGDG. Extension experimental results show the superior performance of using our methods on dialogue metrics and knowledge correlations.
Limitations
Our limitations are as follow:
• Data Scale: This paper only employ the Wikipedia of Wizard dataset, a small scale and well-established knowledge conversation dataset, and lack of the validation on largescale dataset.
• Backbones: This paper lacks the evaluating of other knowledge dialogue model on the proposed method. Actually, we have two reasons to employ the PLATO. First, the PLATO can better handle the one-to-many phenomenon, which is suitable for learning our expansion samples. Second, the PLATO is a pre-trained dialogue model, and its performance on knowledge dialogue generation task has been proved. We will evaluating the performance of other knowledge dialogue model on our method for our future work.
• Knowledge Expansion Methods: This paper only use the synonym and antonym to construct the noised knowledge, which lacks of the comparison of using other data augment method. Indeed, we use two tokenlevel data augmentation methods (synonym and antonym augmentation) to prove our statements on hallucination problem in knowledgedialogue generation task. Based on this study, we believe that incorporating other data augmentation methods will also mitigate the hallucinations.
• Manual Prompts and Responses: This paper designed five prefix prompts, four post-prompts and nineteen euphemistic responses. For AK-More method, we simply randomly choose one prefix-prompt and one post-prompt and concatenate them with the ground-truth response. This leads to some irregular responses. As for CK method, we randomly select one euphemistic response for the incorrect knowledge. However, we found that the response may not coherent with the query. We will design more smooth expansion ways to construct more human-like training samples for our future work.
Ethics Statement
We acknowledge and ensure that our study is compatible with the provided Code of Ethics.
Knowledge-grounded open-domain dialogue generation is crucial for building a knowledgeable dialogue system, which is beyond the wildest dreams in natural language process field. All our experiments are conducted on public available datasets to avoid ethical concerns. All terms for using these datasets are strictly followed in our study. There are no direct ethical concerns in our research.

heading: Acknowledgments
text: We would like to thank the anonymous reviewers for their constructive comments. This research is supported by Beijing Natural Science Foundation (No.4222037 and L181010) and BIT Research and Innovation Promoting Project (Grant No.2022YCXY021). Kan Li is the corresponding author.

heading: A Prefix and Post Prompts
text: We manually design five prefix prompts and four post prompts, which are shown in Table 7. We discuss below about the prefixes and posts.
We designed the prefixes and posts based on the WoW dataset and our daily conversation habits. In WoW dataset, one role is “0_Wizard”, and the other
is “1_Apprentice”. We noticed that the 1_Apprentice will give the sentences such as “correct my if I am wrong . . . ”, which is also easy to appear in our daily conversation. Taking inspiration of this, we manually designed the prefixes and posts. Moreover, since the PLATO is pre-trained on conversation datasets, these prefixes may introduce the pre-knowledge that the model learned during the pre-training process.
In fact, we declare the weakness of our manual prefixes and posts, i.e. direct connections of prefixes, responses, and posts do not fit all contexts. Therefore, we are exploring a new way of constructing replies, such as passing the design prefix, response, post, and context into the largelanguage-model to rewrite the appropriate response. We believe that better prefixes and posts will lead to more benefits in solving the hallucination problem.

heading: B Euphemistic Responses
text: We manually design nineteen euphemistic responses, which are shown in Table 8.

heading: C Dissuasion about the boundary between ak-less and ak-more
text: Below we provide an example in our dataset:
• Ground-truth Knowledge: laziness | tesis (" thesis ") is a 1996 spanish thriller film.
• AK-Less Knowledge: acedia | tesis ("thesis") is a 1996 spanish thriller film.
• AK_More Knowledge: laziness | tesis ("thesis") personate a 1996 spanish thriller picture show.
It can be noted that the more synonyms are introduced into a sentence, the semantics of the sentence will become more and more different from the original semantics. Therefore, we suppose that replacing at least 30% of words at once will make a big difference in sentence semantics. Then, we decided the boundary between ak-less and ak-more.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: 3 A1. Did you describe the limitations of your work?
We provide a section of Limitations after the Conclusion and before the Ethics Statement

heading: A2. Did you discuss any potential risks of your work?
text: Not applicable. Left blank.
3 A3. Do the abstract and introduction summarize the paper’s main claims? 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 7 Did you use or create scientific artifacts? Left blank.

heading: B1. Did you cite the creators of artifacts you used?
text: Not applicable. Left blank.

heading: B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: Not applicable. Left blank.

heading: B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank.
7 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We use a publicly well-established dataset.

heading: B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank.
7 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank.
C 3 Did you run computational experiments? 3
7 C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We use the released code and checkpoints. We cite the source of our model.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Not applicable. Left blank.

heading: C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Not applicable. Left blank.
3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 3
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? 3

heading: D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank.

heading: D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Not applicable. Left blank.
3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? 2

heading: D4. Was the data collection protocol approved (or determined exempt) by an ethics review board?
text: Not applicable. Left blank.

heading: D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?
text: Not applicable. Left blank.

heading: Future Work
text: We will evaluating the performance of other knowledge dialogue model on our method for our future work. • Knowledge Expansion Methods: This paper only use the synonym and antonym to construct the noised knowledge, which lacks of the comparison of using other data augment method. Indeed, we use two tokenlevel data augmentation methods (synonym and antonym augmentation) to prove our statements on hallucination problem in knowledgedialogue generation task. Based on this study, we believe that incorporating other data augmentation methods will also mitigate the hallucinations. • Manual Prompts and Responses: This paper designed five prefix prompts, four post-prompts and nineteen euphemistic responses. For AK-More method, we simply randomly choose one prefix-prompt and one post-prompt and concatenate them with the ground-truth response. This leads to some irregular responses. As for CK method, we randomly select one euphemistic response for the incorrect knowledge. However, we found that the response may not coherent with the query. We will design more smooth expansion ways to construct more human-like training samples for our future work