Neural network language models Vaswani et al. (2017); Radford et al. (2019) have been increasingly adopted as a central part of open-domain dialogue systems Wolf et al. (2019); Zhang et al. (2020); Roller et al. (2020); Adiwardana et al. (2020). Utterances sampled from such language models sound natural, as reflected in these systems’ high scores in human evaluations focused on measures such as “engagingness” or “human-likeness”. While fluent, however, the responses generated by these systems are often only locally coherent or contain confabulated statements (see the red portions of the response in Figure 1 for illustration).
In this work, we introduce a new classification task and benchmark for evaluating knowledge-grounded dialogue systems, systems that are expected to conduct a conversation based on a particular source of information, with the goal of making unstructured information more accessible to a user. It is critical for such a system to avoid producing utterances that appear to convey information but in reality are not supported by the document or even contradict it. The system should also avoid responses that fail to respond to the user’s question, because they are accurate but off-topic (e.g., “The University of Michigan is located in Ann Arbor”), or because they are excessively general (e.g., “I don’t know much about NYC” in Figure 1).
Existing automatic evaluation metrics for dialog, such as BLEU Papineni et al. (2002), ROUGE Lin (2004) and MAUDE Sinha et al. (2020), are ill-suited to detecting these issues, and correlate poorly with human judgments Liu et al. (2016); Dziri et al. (2019); Sai et al. (2019). To facilitate progress towards reliable evaluation metrics for grounded dialog, we propose a new classification task extending the Natural Language Inference (NLI) paradigm Dagan et al. (2005); Bowman et al. (2015); Williams et al. (2018). NLI seeks to determine, given a premise and a hypothesis , whether entails , contradicts it, or is neutral with respect to it. For our taxonomy, we adopt the entailment and contradiction labels from the NLI paradigm, but split the neutral label into three sub-categories: hallucination responses, which are topical but include unverifiable information; off-topic responses; and generic responses, which are too vague to be verified. As a testbed for evaluation metrics based on this taxonomy, we create the Benchmark for Evaluation of Grounded INteraction (BEGIN), a dataset of 8113 dialogue responses generated by language models fine-tuned on the Wizard of Wikipedia dataset Dinan et al. (2019), and ask annotators to categorize these responses using the proposed taxonomy.
We establish baseline performance on this benchmark using two pretrained transformer models, BERT Devlin et al. (2019) and T5 Raffel et al. (2020). We fine-tune them on two existing NLI datasets: MNLI Williams et al. (2018) and DNLI Welleck et al. (2019). As these NLI datasets only support the coarse-grained distinction between entailment, contradiction and neutral
, we additionally use perturbation techniques to generate examples for each of the five categories of our taxonomy, and fine-tune the pretrained models on this extended dataset. We find that there is considerable room for improvement between the best model we train (slightly above 70%) and human performance estimated from inter-annotator agreement on the benchmark (around 90% agreement with majority), suggesting that there are opportunities for developing stronger metrics for grounded dialog evaluation.
The main contributions of this work are:
We propose a taxonomy of responses generated by a knowledge-grounded conversation system; this taxonomy extends the Natural Language Inference framework.
We present a new benchmark, BEGIN, consisting of knowledge-grounded dialogue system responses annotated according to this taxonomy.
We establish baseline performance on BEGIN using BERT and T5 fine-tuned on standard NLI datasets, and improve upon it using our own adversarially-created dataset.
2 Constructing the BEGIN Dataset
To generate dialogue responses for the BEGIN dataset, we fine-tuned two dialogue agents on an existing knowledge-grounded dialogue dataset. Based on error patterns commonly produced by these dialogue agents, we created a taxonomy of grounded dialog responses. We then constructed BEGIN by asking human annotators to categorize a sample of dialogue system responses according to this taxonomy.
2.1 Training Knowledge-Grounded Agents
We fine-tune our knowledge-grounded dialogue models on the Wizard of Wikipedia dataset (WoW; Dinan et al. 2019). WoW consists of crowdsourced English dialogues between a “Wizard” and an “Apprentice”, where the goal of the Wizard is to convey to the apprentice information about a particular topic. The Apprentice, in turn, is expected to seek information about the topic. At each turn of the conversation, the Wizard is presented with passages from Wikipedia and chooses an evidence span (typically a sentence) to use as supporting evidence in their response. Not all utterances in WoW are grounded in external evidence: unlike the Wizard, the Apprentice is not presented with Wikipedia sentences when produce an utterance; and the Wizard is allowed to produce an utterance that does not use the evidence.
We trained our models to generate the Wizard’s response based on a concatenation of two inputs: an evidence span (the Wikipedia sentence presented to the Wizard) and the previous dialogue turn, produced by the Apprentice. We omitted (evidence span, previous turn, response) triples in which the Wizard did not explicitly select a passage as evidence for the response. We used 82722 triples for training, 8800 triples for development, and 8690 triples for test.
We fine-tuned the base GPT-2 model Radford et al. (2019) and the base version of T5 (Raffel et al., 2020) on the WoW dataset. We filtered out responses that the Google Perspective API deemed to have a greater than likelihood of containing toxic language.
For GPT-2, we framed response generation as language modeling (cf. DialogGPT, Zhang et al. 2020): we concatenated the evidence, the previous turn and the response, and continued training GPT-2 using cross-entropy over the response tokens. We used the same hyper-parameters as in wolf2019transfertransfo. We refer to this model as GPT2-WoW.
For T5, which is an encoder-decoder model, the encoder was provided with the concatenation of the evidence span and the previous turn, and the decoder was trained to predict each token in the response given the previous tokens. We used the hyperparameters found using grid search on the dev set. We refer to this model asT5-WoW. Neither of these fine-tuned models is intended to advance the state of the art in grounded dialogue generation; rather, we used them to investigate the errors made by typical neural dialogue systems.
2.2 Response Taxonomy for Grounded dialogue Systems
A manual inspection of 200 of the responses generated by GPT2-WoW revealed that, in addition to responses that were entailed by the document as desired, there were five common error types. This section defines and illustrates each of the six response types using examples from the manually inspected sample of responses.
Entailed responses are those that are faithfully supported by the evidence span:
Evidence: Robert Norman Ross (October 29, 1942 – July 4, 1995) was an American painter, art instructor, and television host.
Previous turn: I would like to know more about Bob Ross.
Response: Yes, absolutely! He was an American painter and television host.
Hallucinated responses are topical, but state information that cannot be verified using the source document, e.g.:
Evidence: The Tucson metro area is served by many local television stations and is the largest designated market area (DMA) in the U.S. with 433,310 homes (0.39% of the total U.S).
Previous turn: Have you ever heard about the Tuscon metro area?
Response: Yes, one of the most visited US cities is the Tucson metro which was largely influenced by the television culture.
We also include in the hallucination category responses that convey personal or subjective opinions, experiences, feelings, and so on, as such statements cannot be verified using the given evidence; some examples are I love to take walks in the park, I’ve been to the hospital before, or I think Justin Trudeau won the elections in 2015. While such responses may be acceptable in human dialogues, in the context of a knowledge-grounded dialogue system we consider them to be hallucinations.
These are neutral sentences that are general enough to fit into a large number of possible contexts. Even if they are ostensibly about the same topic as the document, they are vague and do not provide information (true or not), e.g.:
Evidence: A ski is a narrow strip of semi-rigid material worn underfoot to glide over snow.
Previous turn: Skiing is really a lot of fun. Nothing like finding some fresh powder.
Response: It seems you might like skiing in the winter.
In contrast to hallucinations, which are semantically close to the evidence but add small amounts of unverifiable information, off-topic responses are unrelated to the evidence, e.g.:
Evidence: Toyota is the world’s market leader in sales of hybrid electric vehicles, and one of the largest companies to encourage the mass - market adoption of hybrid vehicles across the globe.
Previous turn: Hi. I have never had a Toyota but I know a lot of people like them.
Response: The era of manufactured fibers began with the development of rayon in France in the 1890s..
These are informative sentences that directly contradict the evidence, e.g.:
Evidence: Although most law schools only offer the traditional three - year program, several U.S. law schools offer an Accelerated JD program.
Previous turn: Because I did not want to go through all the schooling? Do you know how long law school is?
Response: Most law schools only offer a 10 year program .
Human dialogues are characterized by cooperative responses that follow the conversational expectations set out by Grice’s maxims Grice (1989): typical speakers do not intentionally mislead the listener, change the topic, or act unhelpfully in any other way. Dialogue system responses may be inappropriate if they are not cooperative in this sense, even when they are entailed by the evidence document; for example:
Evidence: Starbucks was founded in Seattle, Washington in 1971.
Previous turn: I love eating at Starbucks, have you been there?
Response: The original location of Starbucks is in Seattle, which was founded in 1971.
Ultimately, dialogue evaluation metrics should take cooperativeness into account, and as such we asked our raters to score the cooperativeness of dialogue responses. For the time being, however, we limit the scope of our experiments below to evaluating faithfulness to the document, and leave modeling cooperativeness to future work.
|Contradiction||0.6%||Evidence:||Broccoli is often boiled or steamed but may be eaten raw.|
|Previous turn:||It actually does. vegetable of course, like broccoli and spinach.|
|Response:||Broccoli can be eaten raw but not boiled|
|Off-topic||6.1%||Evidence:||A programming language is a formal language that specifies a set of instructions that can be used to produce various kinds of output.|
|Previous turn:||Is it programming?|
|Response:||Yes and I think that is why the United States still has the highest population of any country|
|Generic||27.5%||Evidence:||Almost as soon as automobiles had been invented, races of various sorts were organised, with the first recorded as early as 1867.|
|Previous turn:||That’s so cool. I guess that’s just our competitive spirit.|
|Response:||That’s what makes it such a interesting history to behold|
|Hallucination||32.2%||Evidence:||Generally, the heavier the ball, the more pins that will topple on two equivalent shots.|
|Previous Turn:||Yea but sadly bowling just isn’t as demanding and difficult a sport as football so its fallen off while football is as prominent as ever.|
|Response:||When they first came out in the 70s, they used to have at least ten percent of the ball on two equivalent shots|
|Entailment||33.6%||Evidence:||Elvis Aaron Presley (January 8, 1935 – August 16, 1977) was an American singer, musician, and actor.|
|Previous turn:||I like to listen to Elvis, the king of rock and roll.|
|Response:||Elvis Presley was born in 1935, and his death was August 16, 1977|
2.3 Annotation Procedure
Rather than asking raters to explicitly classify responses based on the proposed taxonomy, we broke down the task into hierarchical questions with Likert scales (from 1 to 5). We summarize this procedure below, and provide the exact questions in AppendixA. Responses often consist of multiple sentences; because each sentence may display different degrees of faithfulness, we asked annotators to rate each sentence in the response separately.
First, we asked annotators to judge whether the response was about the same topic as the evidence, and if not, whether it was best described as generic or off-topic. We additionally asked them if the response was cooperative. In the next stage, we asked the raters if the response was objective or contained personal and subjective opinions. If the rater judged that the response was objective, we asked them if in their judgment the response was intended to provide information, about the evidence in the document or anything else. If the answer to the last question was affirmative, we presented them with two follow-up questions: first, we asked them if the response was fully supported by the evidence; and second, we asked if any part of the response contradicted the evidence.
We collected annotations for 8113 dialogue responses, which we split into a development (10% of the examples) and test (90% of examples) set; we release the data at https://github.com/google/BEGIN-dataset. Examples were randomly divided into dev. and test set partitions in such a way that examples using the same input context would only appear in the same partition. We did not create a training set to discourage the development of evaluation metrics that overfit to the specific features of BEGIN.
In post-processing, we converted the numerical ratings assigned by the annotators to one of our category labels using the procedure described in Appendix B. We note that the categories are not always mutually exclusive. For example, in a conversation about bees, the response They have pretty big personalities would be both off-topic and generic. The appropriate label may also depend on linguistic ambiguity that cannot be resolved from the context given to the annotators. In one example, the response Oppenheimer as he is known as I think in neonatal med / ophthalmology was generated about a document that says He was the Director of Pediatric Neurosurgery at Johns Hopkins Hospital in Maryland from 1984 until his retirement in 2013. Because the pronoun he
in the document doesn’t resolve to an antecedent, it is hard to determine whether this utterance is better described as a hallucination (attributing a medical specialty not mentioned in the document) or off-topic (this document was probably not about Oppenheimer at all), which is what the rater ultimately selected. Based on the annotations, 78% of the generic responses and 71% of the off-topic responses in our development set may also contain hallucinated information, but we label these overlapping cases as generic or off-topic respectively since these broader issues often subsume the hallucination problems.
We include examples from the development set in Table 1 along with the label breakdown. We note that the labels are unevenly distributed. Hallucinations and generic comments make up two of the biggest categories of responses. By contrast, contradictions make up a small fraction of the distribution. This suggests that LM-based dialogue agents like GPT2-WoW and T5-WoW are more likely to add extra confabulated information rather than directly contradict the evidence.
To evaluate inter-annotator agreement, we obtained two additional annotations (from the same pool of raters) for approximately 15% of the responses. The average Krippendorf’s alpha on the responses to the different questions was around 0.41. This denotes relatively low-to-moderate agreement. One factor that may impact the scoring is slight disagreements between Likert scores (such as the difference between a 4 and a 5) which were counted as “partial” agreements scaled by the distance between the responses. Further reducing agreement are cases where a disagreement in an earlier question propagated to the follow-up questions (e.g. they answered differently to whether the response was intended to be informative and so one of them left blank the follow-up question on how supported the information is) but we counted agreement on each of these questions as if they were independent of each other. Another factor that may affect human performance is ambiguous cases or instances where response categories are not always mutually exclusive, as mentioned above.
3 Classifying Responses Using Existing NLI Datasets
We establish baseline performance on BEGIN using models based on BERT Devlin et al. (2019) and T5 Raffel et al. (2020). We first fine-tune the models on existing NLI datasets. Since these datasets are only labelled for the traditional three-way NLI classification (entailment, contradiction and neutral), in these experiments we collapse the three sub-labels that correspond to neutral in our fine-grained classification. In Section 4, we train a classifier for the full 5-way categorization scheme by creating adversarial data.
The first dataset we use, MNLI Williams et al. (2018), is a collection of 433k premise-hypothesis pairs, where three hypotheses, one for each label, were generated by crowdworkers based on a premise drawn from a corpus. The second is DNLI Welleck et al. (2019), which consists of 343k pairs of dialogue utterances and “persona attributes” curated from the PersonaChat dataset Zhang et al. (2018) (e.g., a persona-describing attribute like I have two cats may be contradicted by a conversation utterance saying I don’t have any pets). Each persona sentence and each utterance were associated with human-labeled triples (subject, predicate, object). A number of approaches were used to form NLI examples by linking dialogue utterances and persona sentences. For example, each unique pair of sentences that shared the same triple were labeled as entailment and each pair of sentences that were from contradictory triples were labeled as contradiction.
4 Adversarially Augmented Training Set
The experiments described in Section 3 were based on existing NLI datasets, which support a coarse-grained three-way classification, but not the full five-way taxonomy we introduced in Section 2.2. In this section we introduce a strategy to automatically create “silver” training data for a classifier that produces the full taxonomy. We avoid training on BEGIN, because, as we mentioned before, we see it as a test-only dataset; recent work has shown that neural networks can overfit to irrelevant features of the dataset when trained on one part of it and tested on another part. We generated a balanced dataset of 7900 (evidence, dialogue history, response) triples (i.e. each label constituted 20% of the data), using the procedures described for each target label in the remainder of this section. See Table 2 for examples of our silver data.
We use the original human generated responses, but to avoid opinions or subjective experiences, we subsample from the portion of examples where the response doesn’t use first person pronouns (selected from a word list) and at least 25% of the words in the response are in the evidence (to avoid responses that are only tangentially related to the evidence).
Off-topic responses are sampled from WoW responses that are based on other pieces of evidence. To avoid having off-topic responses that would be trivial to spot based on lexical cues, we sample from conversations that were about the same topic as the target conversation.
Generic sentences are generated from GPT2-WoW with a low softmax temperature (0.4).
We perturb evidence spans from the WoW test set and then feed them to GPT2-WoW; in general, this results in responses that could be considered hallucinations with respect to the original evidence (see Table 1(b)). We use three perturbation methods, each applied to a different evidence document. All of these perturbations substantially alter the truth of the sentence while keeping it on topic. First, we swap the subject and the object of the original evidence. Second, we replace up to two verbs in the sentences by verbs of the same tense. Finally, based on an error analysis that showed that most hallucination errors made by our dialogue system involved incorrect entities, we extract all mentioned entities from different dialogue examples using the SpaCy NER tagger Honnibal and Montani (2017), and replace up to two randomly chosen entities in the original evidence document with entities of the same type (e.g., Person, Location or Organization).
Adversarially-generated contradiction examples include two types of cases. The first is negated sentences, created based on an English Resource Grammar (ERG) parse Flickinger et al. (2014); for example, The skateboarding market is estimated to be around eight billion dollars was replaced with The skateboarding market is not estimated to be around eight billion dollars. In the second type of case, adjectives are replaced with their WordNet antonyms Miller (1998): Ancient Greece was home to the first pentathlon is replaced with Ancient Greece was home to the last pentathlon that was documented.
Unlike hallucination examples, where we perturb the document and then feed it to GPT2-WoW, the contradiction examples are generated by directly perturbing the human response from the WoW dataset: initial experiments indicated that GPT2-WoW is not sensitive to these perturbations when applied to the evidence, in contrast with its sensitivity to the more substantial perturbation we used to generate hallucination examples. This indicates that this dialogue systems responses are only grounded in the document to a fairly limited extent.
|Development set||Test set|
5 Experimental Set-up
Each classifier takes as input the context—the evidence and the previous conversation turn—concatenated with a separating delimiter and the response. For BERT, we train a three-way or five-way classifier over the output [CLS] token. For T5, we follow the MNLI set-up used in the paper that introduced T5 Raffel et al. (2020): the string “premise: ” is concatenated with the context, the string “hypothesis: ” is concatenated with the response, and the concatenation of the two strings is then passed as input to T5.
In addition to separate experiments evaluating models fine-tuned on MNLI and models fine-tuned on our adversarially augmented training data, we also investigated the performance of models trained first on MNLI and then on our adversarial data.
All models were trained with a batch size of 32 over 3 epochs, using the Adam optimizer and a learning rate of. We evaluated the classifiers’ performance via accuracy and macro-averaged F1 (i.e. computing F1 on each category before averaging) on BEGIN.
Table 3 summarizes the results of our experiments. MNLI is clearly a better fit to this task than DNLI. Fine-tuning on the adversarial data on its own is fairly effective even though it is a significantly smaller resource than DNLI or MNLI. Finally, fine-tuning first on MNLI and then on the adversarial data produces higher accuracy than training on the adversarial data alone.
None of our models exceeds 71% accuracy in the three-way classification setting, or 46.5% accuracy in five-way classification. By comparing individual annotator ratings and the majority-voted label in the triple-annotated subset of BEGIN, we estimate that human accuracy is about 90% for 3-way classification and 75% for 5-way classification. While humans do not agree perfectly, there is still a lot of room for improvement between these models and human performance. Finally, we note that accuracy is similar across BERT and T5, despite the fact that T5 contains orders of magnitude more parameters and was pretrained on a much larger corpus. This suggests that scaling up the pretrained model may not be sufficient to make progress on this task using the training datasets we have explored.
6.1 Error Analysis
In Table 4, we show some examples of errors made by different BERT-based models. One possible source of mistakes is misleading lexical cues. In the first example, all three models label the response as entailed, which may be due to the high degree of lexical overlap with the evidence. However, the semantic meaning of the information in the response is a bit different from the evidence, which is why humans annotated it as a hallucination. In the second example, two of the BERT models misidentify the response as a contradiction, which may be due to the models relying too much on the negative word didn’t in the response.
|Evidence:||Early skiers used one long pole or spear.||G: Hallucination M: Entailment A: Entailment MA: Entailment|
|Previous turn:||That’s amazing. i sometimes wonder why it’s not as popular as football, basketball and the rest though. it’s an amazing sport|
|Response:||they were the first to use a long pole or spear|
|Evidence:||Historically, archery has been used for hunting and combat.||G: Hallucination M: Contradiction A: Contradiction MA: Hallucination|
|Previous turn:||Yes it does take skill. I did not know what I was doing and ended up burning or scratching the inside of my arm with the tail of the arrow! Im not sure if its something you can develop with practice or not|
|Response:||You did’nt do archery while hunting or doing combat !|
|Evidence:||Skiing can be a means of transport, a recreational activity or a competitive winter sport in which the participant uses skis to glide on snow.||G: Off-topic M: Neutral A: Hallucination MA: Hallucination|
|Previous turn:||yea seems easy but it isnt|
|Response:||The easiest way to get in shape is to go down to the slopes of a volcano and practice snowboarding|
|Evidence:||Cheerleading is an activity wherein the participants (referred to as “cheerleaders”) cheer for their team as a form of encouragement.||G: Hallucination M: Neutral A: Off-topic MA: Off-topic|
|Previous turn:||I did a little bit of cheerleading when i was younger. how about you?|
|Response:||No but i do have a lot of experience cheering for my team in the olympics.|
|Evidence:||Skiing can be a means of transport, a recreational activity or a competitive winter sport in which the participant uses skis to glide on snow.||G: Generic M: Contradiction A: Off-topic MA: Off-topic|
|Previous turn:||My brother is a professional skier.|
|Response||I have never tried skiing.|
6.2 Label Confusion
Figure 2 compares the predictions of three fine-tuned versions of BERT to the gold labels. All three variants are poor at predicting contradiction. The model fine-tuned on MNLI over-predicted contradictions, while the model fine-tuned on the adversarial data has low recall for contradiction prediction. The model trained on both resources performed better at predicting contradictions, but still misclassified most of them as hallucinations. The off-topic examples are also difficult for the models to classify; while they are only rarely confused with contradiction or entailment, they were often confused with the other neutral subcategories (hallucination and generic).
All three models displayed a substantial degree of confusion between the hallucination and entailment labels, but not as much between entailment and generic or off-topic examples. This difficulty of distinguishing hallucinated and faithful information would be obscured in the 3-way NLI categorization scheme where hallucination is aggregated with off-topic and generic. In other words, our 5-way taxonomy makes it possible to identify this as a particular set of neutral examples that is more likely to be confused with entailment.
7 Related Work
NLI for dialogue system evaluation
welleck2018dialogue propose using NLI to improve dialogue consistency. They create the dialogue NLI dataset, composed of (premise, hypothesis) pairs curated from the PersonaChat dataset Zhang et al. (2018) and annotated with textual entailment labels by humans. They demonstrate the effectiveness of models trained on DNLI in re-ranking candidate responses by penalizing responses that contradict so-called “persona sentences”, which express properties of the speaker (I am a vegetarian). dziri2019evaluating also used NLI to evaluate dialogue consistency. They generated a large-scale, noisy synthetic dataset of (premise, hypothesis) pairs tailored for dialog, also based on Zhang2018Personalizing.
Hallucination in neural text generation
The hallucination issue affects a range of tasks that involve generating text from a neural language model Tian et al. (2019); Maynez et al. (2020). Across tasks, such models are typically trained to maximize the likelihood of the reference; at test time, this leads the decoder to produce an output with a high likelihood under the language model, regardless of whether the output is faithful to the input Holtzman et al. (2019); Tian et al. (2019). Previous works attempting to quantify this issue have focused on the task of summarization. For example, kryscinski2020evaluating proposed a synthetic dataset for determining whether a summary is consistent with the source document. Similar to our adversarial training with noisily supervised examples, they train a classifier on a dataset constructed by applying a number of syntactic transformations to reference summaries. Besides the different target task (dialogue in our case), our work differs from kryscinski2020evaluating in two ways: first, we propose a fine-grained categorization of responses tailored for the dialogue task, inspired by a similar effort for abstractive summarization Maynez et al. (2020); and second, we train an evaluation system using an adversarial dataset where responses result from perturbing the grounding document and feeding the result to a dialogue system. An alternative approach for assessing faithfulness in abstractive summarization, which also uses an auxiliary language understanding task, measures whether a question answering system produces the same responses for the source and the summary Durmus et al. (2020); Wang et al. (2020).
In this paper, we introduced a new taxonomy for evaluating the faithfulness of knowledge-grounded systems. We presented the BEGIN benchmark for testing grounded dialog evaluation metrics, consisting of around 8k responses generated by two neural dialogue agents. Lastly, to establish baseline performance on this task, we fine-tuned BERT and T5 to classify a dialog response into one of the five categories of our taxonomy, using existing NLI datasets as well as adversarially created in-domain data. While this baseline performed reasonably well, there is significant room for future work to improve performance on our benchmark, which in turn will lead to stronger metrics for grounded dialog evaluation.
Appendix A Annotation Protocol
We gave each rater a “document” (evidence span coming from Wizard of Wikipedia), a conversation history (previous turn in a conversation coming from a Wizard of Wikipedia test set example) and a generated response (from either WoW-T5 or WoW-GPT2). Raters were asked the following questions (all responses were on a 1–5 Likert scale):
Is this utterance about the same topic as the document?
If not, (score 1–3) then please identify it as either generic, off-topic, both or neither?
Is this a relevant utterance - something that a cooperative communicator, who’s not trying to intentionally mislead, change the topic, or be unhelpful in any other way, would say?
Does this utterance describe any personal experiences or personal opinions?
If not containing personal experiences, then is part of the utterance intended to convey information, regardless of whether it’s true or not?
If so, does the information partially or fully contradict the document?
If so, is all of the information supported by the document?
Annotators were additionally provided with instructions, definitions, and examples to help them answer the questions.
Appendix B Cut-offs for Determining Labels
We derive labels from the annotators’ ratings using the following procedure. If the rater judged in question (1a) that the response was generic or off-topic, we assign that label to the response. Otherwise, if the rater judged that it contained personal information (score in question 2), or that not all of the information is supported by the document (score in question 3.a.ii.), we label the example as a hallucination. If they gave it a score of on the contradiction question (3.a.i.), we label it as contradiction. Finally, If they said that all of the information is supported by the document (score in question 3.a.ii.), we label it as entailment. The procedure ends as soon as a label is assigned, such that generic (for example) takes precedence over hallucination.
We thank Jennimaria Palomaki, Dipanjan Das, Tom Kwiatkowski and Slav Petrov for helpful feedback. We also thank Ashwin Kakarla and his team for helping with the annotations.
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