F aith D ial: A Faithful Benchmark for Information-Seeking Dialogue (2022)

Abstract: The goal of information-seeking dialogue is to respond to seeker queries with natural language utterances that are grounded on knowledge sources. However, dialogue systems often produce unsupported utterances, a phenomenon known as hallucination. To mitigate this behavior, we adopt a data-centric solution and create FaithDial, a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. We observe that FaithDial is more faithful than WoW while also maintaining engaging conversations. We show that FaithDial can serve as training signal for: i) a hallucination critic, which discriminates whether an utterance is faithful or not, and boosts the performance by 12.8 F1 score on the BEGIN benchmark compared to existing datasets for dialogue coherence; ii) high-quality dialogue generation. We benchmark a series of state-of-the-art models and propose an auxiliary contrastive objective that achieves the highest level of faithfulness and abstractiveness based on several automated metrics. Further, we find that the benefits of FaithDial generalize to zero-shot transfer on other datasets, such as CMU-Dog and TopicalChat. Finally, human evaluation reveals that responses generated by models trained on FaithDial are perceived as more interpretable, cooperative, and engaging.

## introduction despite the recent success of knowledge-grounded neural conversational models @xcite @xcite in generating fluent responses, they also generate unverifiable or factually incorrect statements, a phenomenon known as hallucination @xcite @xcite work done while at ibm research. @xcite . ensuring that models are trustworthy is key to deploying them safely in real-world applications, especially in high-stakes domains. in fact, they can unintentionally inflict harm on members of the society with unfounded statements or can be exploited by malicious groups to spread large-scale disinformation. recently, @xcite investigated the underlying roots of this phenomenon and found that the gold-standard conversational datasets @xcite @xcite -upon which the models are commonly fine-tuned-are rife with hallucinations, in more than 60% of the turns. an example of hallucination in wizard of wikipedia (wow; @xcite ) is shown in the red box of figure 1 . in wow, an information seeker aims to learn about a topic and a human wizard harnesses knowledge (typically a sentence) from wikipedia to answer. this behavior, where the human wizard ignores the knowledge snippet and assumes a fictitious persona, can later reverberate in the dialogue system trained on this kind of data. instead, the ideal wizard response, highlighted in green, should acknowledge the bot's nature, and whenever the knowledge is not sufficient or relevant, it should acknowledge its ignorance of the topic. unfortunately, modeling solutions alone cannot remedy the hallucination problem. by mimicking the distributional properties of the data, models are bound to ''parrot'' the hallucinated signals at test time @xcite . what is more, @xcite observe that gpt2 not only replicates, but even amplifies hallucination around 20% when trained on wow. this finding also extends to models that are designed explicitly to be knowledge-grounded @xcite . filtering noisy or high-error data @xcite is also prone to failure as it may either break the cohesion of discourse or it may require excluding entire dialogues. in this work, we adopt instead a data-centric solution to address hallucinations and create faithdial, a new benchmark for faithful 1 knowledge-grounded dialogue. specifically, we ask annotators to amend hallucinated utterances in wow by making them faithful to the corresponding knowledge snippets from wikipedia and acknowledging ignorance when necessary. this approach is vastly more scalable than creating faithdial from scratch while retaining the cohesiveness of conversations. moreover, it allows us to shed light on hallucinations by contrasting corresponding wizard's responses in wow and faithdial. as a result, faithdial contains around 50k turns across 5.5k conversations. extensive human validation reveals that 94.4% of the utterances in faithdial are faithful (i.e., without hallucinations), compared to only 20.9% in wow. moreover, we benchmark several state-of-the-art models @xcite @xcite on dialogue generation. if trained on faithdial, we find that they are significantly more faithful while also 1 faithfulness is sometimes referred to as attribution @xcite or fidelity @xcite . faithdial also provides supervision for hallucination critics, which discriminate whether an utterance is faithful or not. we source positive examples from faithdial and negative examples from wow. compared to other dialogue inference datasets @xcite , the classifiers trained on this data (which we call faithcritic) transfer better to general nlu tasks like mnli @xcite and achieve state-of-the-art on begin @xcite , a dialogue-specific knowledge grounding benchmark in a zero-shot setting. thus, faithdial holds promise to encourage faithfulness in information-seeking dialogue and make virtual assistants both more trustworthy. we release data and code for future research. foot_0 ## faithdial: dataset design given the motivations adduced above, the primary goal of this work is to create a resource for faithful knowledge-grounded dialogue that allows for both training high-quality models and measuring the degree of hallucination of their responses. we define the notion of faithfulness formally as follows: definition 2.1 (faithfulness). given an utterance u n , a dialogue history @xmath0, . . . , u n-1 ), and knowledge @xmath1, . . . , k j ) at turn n, we say that u n is faithful with respect to k iff the following condition holds: hence, an utterance can optionally be grounded on multiple facts but not none. in what follows, we present the design of our task as well as our annotation pipeline to curate faithdial. in our dialogue setting, we simulate interactions between two speakers: an information seeker and a bot wizard. ## experiments the purpose of faithdial is two-fold: first, the collected labels can serve as training data for a critic to determine whether a given response is faithful or hallucinated. the second goal is providing high-quality data to generate faithful responses in information-seeking dialogue. given knowledge k n and the conversation history @xmath2, . . . , u n-1 ), the task is to generate a response u n faithful to k n . we benchmark a series of state-of-the-art dialogue models @xcite @xcite on faithdial. we also evaluate them on wow and in a zero-shot transfer setup on cmu-dog, and topicalchat). we implement all the baselines using the huggingface transformers library @xcite . ## conclusions we release faithdial, a new benchmark for faithful information-seeking dialogue, where a domain-expert bot answers queries based on gold-standard knowledge in a conversational manner. examples are created by manually editing hallucinated and uncooperative responses in wizard of wikipedia (wow), which constitute 79.1% of the original dataset. leveraging the resulting high-quality data, we train both a hallucination critic, which discriminates whether utterances are faithful to the knowledge and achieves a new state of the art on begin, and several dialogue generation models. in particular, we propose strategies to take advantage of both noisy and cleaned data, such as intermediate fine-tuning on wow and an auxiliary contrastive objective. with both automated metrics and human evaluation, we verify that models trained on faithdial drastically enhance faithfulness and abstractiveness, both indomain and during zero-shot transfer to other datasets, such as topicalchat and cmu-dog. a particular edit. below are the questions we ask in every hit: wizard's response contain other information that is not supported by k? (e.g., facts, opinions, feelings) (yes/no) (a) if the response is hallucinated, what is the type of the unsupported information? (options: expressing a personal experience, expressing an opinion, expressing feelings, expressing unsupported facts, giving advice, acknowledging information from the seeker) (b) if the response is hallucinated, was the unsupported information triggered by a question/opinion from the seeker? (yes/no) (c) besides unsupported information, does the wizard's response contain thoughts/ opinions/feelings/facts that are supported by k? (yes/no) (d) modify the wizard's sentence such that the response: i. uses only the facts from k to make the response informative. ii. is not a copy paste of k but a paraphrase of it. iii. is relevant to the previous utterance and cooperative with the seeker. (e) if the response is not hallucinated, does the wizard's response express personal thoughts/opinions/feelings that are supported by k? (yes/no) (f) if the response is not hallucinated, does the wizard's response contain factual/objective information that is supported by k? (yes/no) 2. if the answer is ''no'' to (e) and (f), the response is flagged as generic. we ask the annotators to modify the wizard's sentence such that the response is supported by k. 3. if the response is faithful, workers are asked the following question: is the wizard's response cooperative with the seeker's response? i.e. the wizard does not ignore answering a question, or does not act in any unhelpful way. (a) if yes, no modification is required for the wizard's response. (b) if no, modify the bot sentence such that: i. the response is relevant to the previous utterance and cooperative with the seeker.