With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulness (2023)

Abstract: Conditional language models still generate unfaithful output that is not supported by their input. These unfaithful generations jeopardize trust in real-world applications such as summarization or human-machine interaction, motivating a need for automatic faithfulness metrics. To implement such metrics, NLI models seem attractive, since they solve a strongly related task that comes with a wealth of prior research and data. But recent research suggests that NLI models require costly additional machinery to perform reliably across datasets, e.g., by running inference on a cartesian product of input and generated sentences, or supporting them with a question-generation/answering step. In this work we show that pure NLI models _can_ outperform more complex metrics when combining task-adaptive data augmentation with robust inference procedures. We propose: (1) Augmenting NLI training data toadapt NL inferences to the specificities of faithfulness prediction in dialogue;(2) Making use of both entailment and contradiction probabilities in NLI, and(3) Using Monte-Carlo dropout during inference. Applied to the TRUE benchmark, which combines faithfulness datasets across diverse domains and tasks, our approach strongly improves a vanilla NLI model and significantly outperforms previous work, while showing favourable computational cost.

## introduction conditional language models suffer from a tendency to hallucinate information @xcite , resulting in generations that are not faithful to their input documents, which limits the trustworthiness of such models. this raises a need for automatic faithfulness metrics. in this context, models trained on natural language inference (nli) @xcite are attractive since, intuitively, a generation being faithful implies it must be entailed by the source @xcite . however, pure nli models have seen mixed success in faithfulness evaluation @xcite @xcite . while in recent evaluation on the true benchmark @xcite , which contains datasets from knowledge-grounded dialogue, summarization and paraphrasing, nliderived metrics perform best overall, they require impractically large models, or costly additional machinery such as question generation and answering models at inference, while still showing robustness issues. thus we ask: what is still needed for pure nli models to perform robustly across faithfulness datasets -while remaining cheap enough to serve as a lean and practical evaluation tool? we enhance a relatively small nli model to make it work robustly across tasks in three ways: task-adaptive data augmentation. in nli, a hypothesis must be fully entailed by its supporting premise. however, in faithfulness, not all parts of the generation always need to be grounded. we identify an instance of this phenomenon in dialogue where parts of a turn can fulfill communicative functions such as hedging or establishing emotional connection and are often disregarded in faithfulness annotation. hence, when applying nli models to complete dialogue turns that may include statements irrelevant for grounding, we run a risk of producing incorrect unfaithfulness predictions. to alleviate this issue, we propose a simple data augmentation method to adapt nli models to genres where they need to be aware of statements that must be exempt from nli-based faithfulness evaluation. our approach is computationally attractive, as it avoids an increase of cost at inference time. integration of nli contradiction scores. existing nli faithfulness metrics typically use the entailment score for their predictions @xcite @xcite . however, @xcite show that subtracting the contradiction score from the entail-ment score (referred to as e-c ) can improve nli performance in certain evaluation tasks. we show that there also is a strong positive effect of e-c for faithfulness prediction, and demonstrate that this is due to a high contradiction probability being a more reliable predictor of unfaithfulness than low entailment probability. monte-carlo dropout inference. applying nli models to faithfulness prediction involves a domain shift from largely human-written data to automatically generated text. to make nli model scores more robust under this shift, we propose to use monte-carlo dropout during inference @xcite . this essentially creates a cheap ensemble and has been shown to deal better with noisy labels @xcite . this approach leads to consistent score improvements in our tasks. the combination of all modifications not only strongly improves over a baseline nli model, but also outperforms all other metrics on true, on average, while being cheaper and smaller. foot_0 2 method details ## experimental setup we run experiments on true @xcite , a benchmark that compiles a wide variety of faithfulness tasks in a standardized format. it contains summarization @xcite @xcite , knowledge-grounded dialog @xcite @xcite foot_2 and paraphrasing (zhang et al., 2019) datasets. 3 following recommendations in true, we evaluate using area under the roc curve (auc). as our base model, we use the deberta-large @xcite ) model of @xcite , trained on multinli @xcite , fever-nli @xcite , anli @xcite , lingnli @xcite and wanli @xcite . the metric all uses all three of our proposed modifications to base. we also investigate a variant without mc dropout inference (-mc) as a more cost efficient alternative. we compare to the strongest models on true: t5 anli @xcite is a t5-11b @xcite ) model trained on anli. 4 summaczs @xcite ) evaluates an nli model on all pairs of input and generated sentences and then averages maximum entailment probabilities for each generated sentence. q2 @xcite combines a question generation/answering pipeline with an nli score. finally, @xcite introduce a strong ensemble of these 3 methods (eorig). to further verify our approach, we construct a new ensemble (eour) by replacing t5 with all. base on six out of nine corpora, but also significantly outperforms all other competitors on average, while being more computationally efficient. as expected, we find the biggest gains in dialogue, where the all model even outperforms eorig on 2 out of 3 corpora. we do not improve on begin, which is likely due to bias in the dataset construction, which we elaborate on in section 5.1. on the summarization part, all improves significantly over base on 3 out of 5 corpora, while not significantly harming performance on any corpus. however, it still falls short of the best models in true. the strong showing of t5 on these corpora suggests that this might be alleviated with a stronger base model. overall, a very similar behaviour is exhibited by -mc, presenting an attractive option when the added overhead of multiple samples is undesirable. eour is on par with eorig, despite massively reduced costs; it even significantly outperforms it on two dialog and the paraphrasing corpora. we also investigate the performance of each individual modification to our model ( outside of dialogue, we find that the augmentation methods have a positive impact on paws, as well as all summarization corpora that are at least partially based on summaries for the cnn/dm dataset @xcite (frank, qags-c, and summeval). while we do not have a definitive explanation for this phenomenon, we hypothesize that on these datasets our augmentations aid in making the model robust in the presence of noise corpus +e-c +mc +aug. frank -0.0+0.3+0.5 +0.1+0.9+1.8 +0.3+1.0+1.7 mnbm -2.1-0.8+0.5 +1.4+2.1+2.9 -0.4+0.0+0.6 summeval +0.7+1.0+1.3 +0.1+1.2+2.3 +0.6+1.6+2.6 qags-x -0.4+0.3+0.9 -1.5-0.2+1.1 -0.3+0.9+2.1 qags-c +0.5+1.2+2.0 -1.6-0.1+1.5 +2.2+3.5+5.0 begin -3.0-1.1+0.6 +0.0+0.6+1.3 -1.6-1.0-0.5 dialfact +8.3+9.1+9.9 +1.1+1.3+1.5 +3.1+3.3+3.5 q2 +5.1+6.5+7.9 -0.4-0.0+0.4 +3.5+4.2+5.0 paws +0.3+0.4+0.5 +1.1+1.3+1.4 +0.8+0.9+1.0 avg +1.6+1.9+2.2 +0.5+0.8+1.1 +1.4+1.6+1.9 or irrelevant context since our augmentations are label-neutral and must similarly be 'ignored' during training. ## related work previous work on the utility of nli for faithfulness led to mixed conclusions. in summarization, @xcite and @xcite find out-of-the-box models have only limited utility in a faithfulness setting. in @xcite , an nli model is outperformed by a question generation/answering (qa/qg)-based method. in contrast, @xcite find that a similar nli model vastly outperforms a qa/qg metric on their data. in knowledge-grounded dialogue, @xcite and @xcite find out-of-the-box models underperform. to improve nli models for faithfulness in summarization, @xcite propose factcc, which is trained on artificially noised summaries. @xcite propose a controllable generation model to generate artificial faithfulness data. in knowledge-grounded dialogue, @xcite and @xcite combine noising techniques to generate additional training data for nli-based faithfulness models. in contrast to our work, these approaches a) generate training data from external sources, instead of directly augmenting nli data, and b) do not explicitly focus on reconciling differences between nli and faithfulness with their augmentation. outside of augmentationbased approaches, @xcite propose to train nli models to label faithfulness at the dependency arc level. ## conclusion we have demonstrated that with a small number of focused adaptations, even a relatively small nli model can robustly predict faithfulness. we have: our improved nli model significantly improves over its baseline across many corpora and outperforms all competitors in average score on true, while being much more efficient at inference. our work suggests that strong improvements are possible for nli-based faithfulness metrics, by combining data augmentation with adapted nli score computation. we hope this finding will spurn advances in cheap and robust nli for faithfulness.