T rue T eacher: Learning Factual Consistency Evaluation with Large Language Models (2023)

Abstract: Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbed human-written summaries, which often differ in their characteristics from real model-generated summaries and have limited coverage of possible factual errors. Alternatively, large language models (LLMs) have recently shown promising results in directly evaluating generative tasks, but are too computationally expensive for practical use. Motivated by these limitations, we introduce TrueTeacher, a method for generating synthetic data by annotating diverse model-generated summaries using a LLM. Unlike prior work, TrueTeacher does not rely on human-written summaries, and is multilingual by nature. Experiments on the TRUE benchmark show that a student model trained using our data, substantially outperforms both the state-of-the-art model with similar capacity, and the LLM teacher. In a systematic study, we compare TrueTeacher to existing synthetic data generation methods and demonstrate its superiority and robustness to domain-shift. We also show that our method generalizes to multilingual scenarios. Lastly, we release our large scale synthetic dataset (1.4M examples), generated using TrueTeacher, and a checkpoint trained on this data.

## introduction generative summarization models are prone to generate summaries that are factually inconsistent with respect to the corresponding input documents @xcite , limiting their applicability in real-world scenarios. since factual consistency evaluation could be cast as a natural language inference (nli) task, nli models are often used to evaluate consistency @xcite @xcite . however, nli models exhibit limited success in evaluating factual consistency in summarization @xcite , since nli datasets lack the entailment phenomena that naturally arise in abstractive summarization @xcite . for example, single-sentence premise-hypothesis pairs are shorter than document-summary pairs @xcite . to address this domain mismatch, previous work proposed various approaches for generating synthetic training data @xcite @xcite . the data is typically generated by perturb-ing human-written summaries to introduce factual inconsistencies. while these perturbations are effective, they are limited to factual error categories that can be covered by the perturbation logic. in addition, since simulating factual errors is challenging, such perturbations may fail to introduce factual errors, leading to incorrect labels. 2 finally, since the synthetic summaries are based on humanwritten summaries, they may differ in style from real model-generated summaries, which can reduce the effectiveness of the synthetic data. an alternative approach to augmenting nli models with synthetic data, is to directly prompt large language models (llms) to evaluate factual consistency. recently, there has been a growing evidence for the effectiveness of llms in evaluating generative tasks @xcite @xcite , including factual consistency in summarization @xcite . however, llms are still too computationally expensive to be heavily used in practice. to make the best of both worlds we propose trueteacher, a simple and effective synthetic data generation method that leverages model-generated summaries and the reasoning abilities of llms @xcite . in trueteacher, we first train a diverse collection of summarization models with different capacities. next, we use these models to summarize each document in a given corpus (figure 1 ). the resulting document-summary pairs are then annotated by prompting a llm to predict the corresponding factual consistency label. we apply trueteacher using flan-palm 540b @xcite to generate a large-scale synthetic dataset, which is used to train a student model. experiments on the summarization subset of the true benchmark @xcite show that augmenting existing nli data with trueteacher data improves a state-of-the-art model's roc-auc from 82.7 to 87.8, while maintaining similar model capacity. the resulting model even outperforms its llm teacher, despite the latter having a ×50 larger capacity. we also compare trueteacher to existing synthetic data generation methods. to this end, we design a systematic study to re-evaluate existing methods with a "fair comparison" in a challenging setting. our results indicate that existing approaches fail to generalize to documents derived from a distribution different from the one used for synthetic data generation. in contrast, trueteacher demonstrates robustness by successfully generalizing to documents from new domains. finally, we apply trueteacher to generate multilingual synthetic data. while existing data generation methods are often limited to english @xcite , trueteacher can use a multilingual llm. results on the mface dataset @xcite , show improvements on 35 out of 45 languages when using our method. this demonstrates the usefulness of multilingual synthetic data and the effectiveness of trueteacher in generating such data. to summarize, this work includes the following contributions: ## trueteacher in this section we describe trueteacher, our approach for generating synthetic examples for the task of factual consistency evaluation in summarization. our main motivation is to use factual inconsistencies that occur in real model-generated summaries, instead of relying on perturbed human-written summaries. to this end, we generate a diverse set of summaries using generative summarization models of different capacities, and leverage a llm to label them for factual consistency. some of the generated summaries are expected to contain factual errors, and we hypothesize that a strong-performing llm can generalize to the task and label them with sufficient quality to be useful for training. the usage of model-generated summaries not only yields more realistic texts, but also allows to potentially include rare errors, which can be harder to incorporate with perturbation logic. our data generation process is illustrated in figure 2 . first, we train a variety of summarization models (upper diagram). we use a collection of one or more summarization training sets @xmath0, sd 2 , . . . , sd n } and different pretrained lm @xmath1, lm 2 , . . . , lm m } to finetune a collection of summarization models @xmath2, sm 2 , . . . , sm k }, where @xmath3. 3 using different pretrained lms allows to diversify the expected consistency errors, e.g., errors made by large or small models. the choice of summarization training sets allows to control for the nature of the resulting summaries, e.g., focusing on abstrative training sets to increase output abstractiveness. next, we obtain model-generated summaries and annotate them (lower diagram). we choose a documents corpus @xmath4, d 2 , . . . , d r } and use all the summarization models in sm to summarize all the documents in d, resulting in a collection of modelgenerated output summaries @xmath5,1 , . . . s r,k }, where s i,j is the summary of document d i generated by summarization model sm j . trueteacher does not require gold summaries, which allows it to be used with any collection of documents d, and makes it more scalable than previous methods @xcite @xcite . finally, a llm is prompted to label all the summaries in o for consistency w.r.t. their source documents, resulting with labels {l 1,1 , . . . , l 1,k , . . . l r,k }. foot_2 figure 1 illustrates a real example of this process for a single document d @xmath6. each document, summary, and label (d i , s i,j , l i,j ) are then used as a synthetic example for training a factual consistency classifier. since we leverage llms for labeling, our approach is likely to benefit from the ongoing progress in llms quality. furthermore, previous approaches often rely on language-specific components (e.g., information extraction), which limits their applicability in multiple languages. since recent llms are pretrained on multilingual data, our method can be easily applied to non-english languages, as we show in §5. ## experimental setup we use trueteacher to generate a synthetic dataset for factual consistency evaluation in summarization ( §3.1), and experiment with it to evaluate the effectiveness and usefulness of our method ( §4). ## experiments and analysis our main experiments are in §4.1 and §4.2, followed by various analyses and ablations in §4.3, §4.4, §4.5 and §4.6. we design our experiments to address the following research questions (rqs): ## multi-lingual data generation for factual consistency evaluation utilizing a multilingual llm enables a straightforward application of trueteacher to multiple languages. this contrasts with recent approaches that rely on nlp components only available for highresource languages, e.g., information extraction @xcite . in this section, we examine trueteacher's usefulness for multilingual factual consistency evaluation. we first generate multilingual synthetic data using trueteacher. this time we train a single summarization model by fine tuning mt5-xxl @xcite on xlsum @xcite and use it to summarize documents from wikilingua @xcite , which we then label for consistency with our llm. for the purposes of this experiment we focus on a subset of wikilingua documents in 4 languages: english (en), french (fe), spanish (es) and german (de). 19 . after generating the dataset for these 4 languages, we sample 100k examples, by randomly sampling 25k in each language with balanced labels (as illustrated in ## related work previous work proposed methods for generating synthetic training data for factual consistency evaluation, by perturbing gold summaries @xcite @xcite @xcite . 21 a key advantage of trueteacher, is the ability to leverage real model-generated summaries, leading to superior performance and robustness. the utility of model-generated outputs was also highlighted by @xcite , who proposed a weakly super-vised consistency evaluation model that leverages probabilistic labels derived from aggregated scores of other consistency evaluation models. our work proposes a simpler solution, that is also inherently multilingual. another line of work for adapting nli-based models for summarization, focuses on better processing of long texts, splitting the documents into sentences to create shorter premise-hypothesis pairs @xcite . recent work attempts to assess llms' capability for evaluating generative tasks @xcite @xcite . @xcite evaluated chatgpt (openai, 2022) speciffically on the task of factual consistency evaluation in summarization. yet, @xcite argued that chatgpt's "closed" nature risks data leakage (training-test contamination). 22 chen et al. ( 2023 ) performed a study of llms as factual consistency evaluators, using a variety of prompting methods. previous work also attempted to distill knowledge from llms @xcite , as well as to leverage llms for data annotation @xcite , and synthetic data generation @xcite @xcite . as far as we aware, our work is the first to leverage llms for data generation for factual consistency evaluation. ## conclusion we introduced trueteacher, a simple and highly effective method for generating synthetic data for factual consistency evaluation. instead of perturbation of human-written summaries like done in previous work, trueteacher leverages realistic model-generated summaries, which are annotated by prompting a large language model. using our method, we generate a large-scale synthetic dataset, which we are making publicly available. our experimental results show that this dataset substantially enhances the performance of a state-of-the-art model. in our systematic study, we compare trueteacher to existing approaches and further demonstrate its effectiveness and robustness. our study highlights the importance of out-ofdomain evaluation, which we hope will be adopted in future work. lastly, we show that trueteacher generalizes well to multilingual scenarios, presenting additional advantage over existing methods.