Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs (2023)

Abstract: A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not faithful to the underlying document. Existing automated metrics used for evaluating the faithfulness of response with respect to the grounding document measure the degree of similarity between the generated response and the document’s content. However, these automated metrics are far from being well aligned with human judgments. Therefore, to improve the measurement of faithfulness, we propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. PMI quantifies the extent to which the document influences the generated response – with a higher PMI indicating a more faithful response. We build upon this idea to create a new decoding technique that incorporates PMI into the response generation process to predict more faithful responses. Our experiments on the BEGIN benchmark demonstrate an improved correlation of our metric with human evaluation. We also show that our decoding technique is effective in generating more faithful responses when compared to standard decoding techniques on a set of publicly available document-grounded dialog datasets.

## introduction document-grounded dialog agents converse with users based on information present in document provided to them. these agents are expected to be factually consistent or faithful to the grounding document and refrain from generating content that cannot be verified using the document. as most existing document-grounded dialog agents @xcite are built by fine-tuning large language models, ensuring faithful response generation is a major challenge. to measure the ability of dialog agents to generate faithful responses, several automatic metrics have been proposed. these metrics take as input the agent generated response and the grounding document to quantify faithfulness. these are based on lexical overlap (e.g., bleu, unigram-f1), semantic overlap (bertscore) or even a trained classifier @xcite . recently, @xcite proposed q 2 , a metric that measures faithfullness using automatic question generation and question answering. a major limitation of existing metrics is that they ignore the crucial dialog history when measuring faithfulness of responses. even though, in many cases, the dialog history provides essential context that is necessary for a complete understanding of the response. to illustrate this point, let's consider two responses, textitr1 and r2 , as depicted in figure 1 . response r1 is self-contained and can be comprehended without relying on the dialog history. on the other hand, response r2 is dependent on the dialog history and can only be fully understood when considering the preceding conversation. unfortunately, current automated metrics do not take into account the dialog history, leading to their failure in evaluating responses that are not self-contained. responses like r2 often lack domainspecific words, making similarity-based metrics like unigram-f1 and bertscore ineffective. additionally, generating question-answer pairs using such responses typically captures incomplete information, rendering metrics like q2 as inadequate measures. to overcome this problem, we propose a new metric that quantifies the faithfulness of a generated response with respect to both the document and the dialog history. our metric is grounded in information theoretic concepts and captures the association of the response with the given document using conditional pointwise mutual information (cpmi). we call our metric pmi-faith, which uses cpmi between the generated response and the document, conditioned on the dialogue history, for quantifying faithfulness. pmi-faith captures the intuition that for a response to be grounded in the document, the probability of its generation given the document should be higher than the probability of its generation without the document. a significant advantage of our metric pmi-faith is that it can be factorized the same way as the likelihood of a response can be factorized in auto regressive models. we take advantage of this property to propose a novel decoding objective, pmi-decode. the goal of pmi-decode is to maximize not just the response's likelihood but a score that combines its likelihood and faithfulness. to summarize, our contributions are threefold: ## related work in this work, we focus primarily on faithfulness aspect of the generated responses with respect to the grounding document. it is crucial to distinguish between faithfulness and hallucination @xcite in evaluating responses. a response is considered faithful only when all the information it contains can be verified or inferred from the grounded document. on the other hand, a response is considered as a hallucination if it provides false or fabricated information. it is important to note that there can be responses that are not hallucinations but are still unfaithful. in such cases, the information provided may not be false, but it cannot be verified using the grounded document as a reference. it is important to point out that the set of faithful responses is a subset of responses that are not hallucinations. in this section, we discuss related work in faithfulness, followed by a brief discussion on mutual information in conversational settings. researchers have used various terms such as faithfulness @xcite , factual consistency @xcite , factual accuracy @xcite , fidelity @xcite , attribution @xcite and hallucination (i.e., the lack of faithfulness) @xcite to define and quantify faithfulness of a model's generated text to a given knowledge. most of the works focusing on evaluating faithfulness propose to train a classifier for the task @xcite @xcite . whereas our proposed metric doesn't require any training and is agnostic to the underlying data. recently, @xcite proposed q 2 for quantifying faithfulness. it uses a question generator to first generate questionanswer (qa) pairs from the generated response. then a qa system is used to find an answer, to the generated question, from the document. finally, an nli system is used to compare the two answers. though q 2 uses the given document to check the faithfulness of a response, it ignores the dialog history. thus, it may fail at handling responses that are non-self contained as depicted in figure 1 . our metric pmi-faith addresses this issue. many recent works @xcite have released different benchmarks that can be used to evaluate the performance of faithfulness metrics. while honovich et al. aim to standardize benchmark datasets across different generation tasks, dziri et al. focus on documentgrounded dialogues, and thus we use their benchmark to compare our metric with various baselines. @xcite and @xcite use mutual information to prevent the conversational models from generating generic responses (such as "sorry, i'm not sure about this topic"). contemporary to our work, @xcite propose to use a conditional pointwise mutual information (cpmi) based metric to evaluate relevance of a generated response with respect to a reference hypothesis for open-domain response generation. in contrast, our work is the first to use cpmi as a metric for evaluating the faithfulness of a response given a document and dialogue history in a document-grounded response generation. ## background in this section, we first review the task of documentgrounded dialog response generation, followed by the definition of the faithfulness metric. ## approach in this section, we describe our proposed metric for faithfulness -pmi-faith. we then propose a decoding strategy pmi-decode based on our metric, with the objective of generating relevant and faithful responses. ## conclusion in this paper, we present a novel metric, pmi-faith, to measure faithfulness of responses generated by document grounded dialog systems. it uses conditional pmi between the response and the document given the dialog history to quantify faithfulness. we extend the idea of pmi-faithto propose a novel decoding objective, pmi-decode which encourages responses to be faithful to the given document by maximizing both the likelihood and faithfulness of the decoded response. our experiments on the begin benchmark prove that our proposed metric better correlates with human judgments compared to existing metrics. on three document-grounded dialog datasets, our novel decoding objective generates more faithful responses than the standard likelihood objective, as measured using automated metrics and a human study.