W e C heck: Strong Factual Consistency Checker via Weakly Supervised Learning (2023)

Abstract: A crucial issue of current text generation models is that they often uncontrollably generate text that is factually inconsistent with inputs. Due to lack of annotated data, existing factual consistency metrics usually train evaluation models on synthetic texts or directly transfer from other related tasks, such as question answering (QA) and natural language inference (NLI).Bias in synthetic text or upstream tasks makes them perform poorly on text actually generated by language models, especially for general evaluation for various tasks. To alleviate this problem, we propose a weakly supervised framework named WeCheck that is directly trained on actual generated samples from language models with weakly annotated labels.WeCheck first utilizes a generative model to infer the factual labels of generated samples by aggregating weak labels from multiple resources.Next, we train a simple noise-aware classification model as the target metric using the inferred weakly supervised information.Comprehensive experiments on various tasks demonstrate the strong performance of WeCheck, achieving an average absolute improvement of 3.3% on the TRUE benchmark over 11B state-of-the-art methods using only 435M parameters.Furthermore, it is up to 30 times faster than previous evaluation methods, greatly improving the accuracy and efficiency of factual consistency evaluation.

## introduction the research of text generation has achieved significant progress in recent years, but it still suffers the main issue of generating output which is factually inconsistent with the given inputs @xcite . to tackle this issue, various metrics have been designed to check the consistency between generated text and the given inputs @xcite . as we know, how to construct such a metric has attracted increasing attention in a variety of fields @xcite , including text summarization @xcite , dialogue generation @xcite , and text simplification @xcite . existing factual metrics can be classified into two types: one based on synthetic data and the other based on task transfer. synthetic-data based metrics @xcite apply data augmentation techniques to construct factual and non-factual texts as positive and negative samples, respectively. metrics trained from these synthetic samples often perform poorly due to the significant mismatch between features of actual generated and synthetic text (e.g. distribution of factual errors) @xcite . task-transfer based metrics utilize the reasoning ability of models trained on relevant upstream tasks, such as natural language inference (nli) @xcite and question answering (qa) @xcite and directly apply them to evaluate factual consistency without any adaption. as described above, previous metrics are learned indirectly from other related resources but without seeing the actual generated text. in such cases, they may overfit to their upstream tasks and fail to generalize to actual generated samples that have significantly different data features. figure 1 illustrates the probability density of three metrics, where the horizontal axis is metric scores and the vertical axis is the score density. though these metrics are comparable in performance, they vary significantly in probability distributions, especially in the xsum dataset, where sample features are greatly different from upstream tasks of these metrics 2 , nli-warmup is extremely confident in predicting both very high and low scores while summac and qafact are only confident in predicting low scores 3 . furthermore, during testing, ensembling different metric scores by simply averaging will further improve their performance @xcite . this also implies that the evaluation metrics learned from different resources are also complementary. to bridge the gap between training and testing and mitigate the scarcity of labeled data, in this paper, we propose wecheck, a factual consistency checking framework based on weakly supervised learning. specifically, wecheck is based on a learning paradigm that provides weak supervision via modeling multiple label sources without access to ground truth. different from previous metrics, wecheck directly utilizes the abundant actual generated samples bootstrapped from models trained on target downstream tasks, e.g. bart on text summarization. then, wecheck follows a two-step pipeline consisting of weak annotation and noiseaware fine-tuning to get the target metric model. in the weak annotation step, by aggregating multiple weak supervision resources, we infer the unknown ground truth label of a sample. to reach this goal, we first provide each sample with a set of weak supervision signals calculated from various other metrics. these metrics are learned from various resources or tasks such as qa-based metrics and nli-based metrics. after unifying and filtering these signals, we train a generative labeling model that models agreements and disagreements 2 in xsum, the summary of each document is abstractive, while existing nli and qa datasets do not have this feature. 3 for more details about these metrics please refer to § 2.3 and §3.2. between them to infer the likelihood of their latent ground truth label. the inferred ground truth likelihood is then treated as a probabilistic label to provide weak supervision. in the second step, we apply noise-aware fine-tuning to train the target metric model. it is noted here, the weak annotation also brings noises to the supervision signal and brings new challenges to the model optimization process. as a solution, we first warmup our target metric model with nli data for a better initialization before weakly supervised training. then, after filtering out samples that are likely to be noisy, we finetune our target metric model with weak annotations. in summary, wecheck could learn how to utilize multiple resources for weak annotation while recognizing and filtering the potential noises accompanied by weak supervision. experimental results show that wecheck not only achieves state-of-the-art performance but also is computationally efficient. on the true benchmark @xcite , which is the current most comprehensive benchmark for factual consistency evaluation, wecheck obtains an average roc auc of 84.8, 3.3% absolute improvement over previous 11b pre-trained task transferred metrics with only a size of 435m parameters. moreover, it's much more stable for various generation tasks, with much lower variance on different tasks. thus, wecheck is a simple but more effective and efficient metric for factual consistency evaluation. we summarize our contributions as follows: ## experimental settings in this section, we introduce the experimental settings of wecheck including the evaluation benchmark, baseline models, and implementation details. ## analysis to analyse how each module and settings work, we conduct analysis experiments on each module and settings of wecheck. training mechanism we first study how the mechanisms proposed in §2 affect the overall framework by removing or replacing them. the results are reported in effects of task we also analyse how each bootstrapped task affect wecheck. in ## labeling model we compare how different data programming based labeling models affect the final metric performance. in wecheck, labeling model p ϕ learns to aggregate multi-resource labels to infer the hidden true label. comparing concretely, our method is similar to snorkel @xcite . because, in our scenario, the number of weak supervision signals is small and their relationships are relatively simple as they are trained from different tasks, we prefer this method over other recent more advanced ones. in from the results in ## related work factual consistency evaluation recently, automatically checking factual consistency has become an increasingly popular topic @xcite . reasoning over a long range of context for factual evaluation is a challenging task that even human annotators may frequently disagree with each other @xcite . thus, it is hard to collect a large-scale high-quality dataset for training a fully supervised model, and previous works search for indirect methods. one branch of them leverage the reasoning ability of nli. based on the model trained on nli datasets, e.g. mnli @xcite , anli @xcite , some works aggregate sentence-level entailment score for checking @xcite , while others adopt document-level nli which directly reasoning over the full context @xcite . another branch of methods apply qa-qg based pipeline for a more fine-grained checking. qags @xcite and feqa @xcite are the earliest attempt on this method, and questeval @xcite and qafacteval @xcite further improve this type of methods by applying nli for answer matching. ## conclusion in this paper, we propose a weakly supervised framework, wecheck, which aggregates weakly supervised signals from multiple resources and trains a target metric model in a noise-aware manner. different from previous metrics that trains from synthetic data or transferred from other tasks, wecheck directly trains with the real generated text. wecheck first annotates each sample with a probabilistic label via a labeling function that aggregates multiple resources. then, in the noise-aware finetuning stage, wecheck applies probabilistic labels to train the target metric model. experimental results show that, wecheck not only surpass previous methods in performance but also time efficient. moreover, wecheck is potential to be compatible with future more stronger metrics, bring further improvements to the overall performance.