Language Models as Fact Checkers? (2020)

Abstract: Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our finetuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.

## introduction pre-trained language models have recently lead to significant advancements in wide variety of nlp tasks, including question-answering, commonsense reasoning, and semantic relatedness @xcite @xcite . these models are typically trained on documents mined from wikipedia (among other websites). recently, a number of works have found that lms store a surprising amount of world knowledge, focusing particularly on the task of open-domain question answering @xcite . in this paper, we explore whether we can leverage the knowledge in lms for fact checking. we propose an approach (fig. 1b ) that replaces the document retriever and evidence selector models in traditional fact-checking (fig. 1a ) with a single language model that generates masked tokens. this offers a number of advantages over the traditional approach: first, the procedure is overall simpler, requiring fewer resources and computation -we do not need to maintain an explicit knowledge base external to our lm, and we do not need an explicit retrieval step. the latter in particular can lead to a huge speedup in the system, since we can skip the time-consuming step of searching over a potentially massive space of documents. second, lms are widely-available and are currently attracting significant research effort. thus, research in language-modeling, particularly in improving lms ability to memorizing knowledge, may also improve the overall effectiveness of our fact-checking pipeline. lastly, our system further shifts the paradigm towards "one model for all" -lms have been used for a wide variety of tasks, and now also for fact checking. in order to determine the feasibility of our approach, we start with a human review study where participants are given a claim from fever @xcite , and are asked to validate the claim using only a bert language model. we found that users had reasonable success in determining claim validity. empowered by the results, we design an end-to-end neural approach for utilizing bert as a fact checker (see figure 1b ). at a high level, we first generate an evidence sentence by masking the claim and using bert to "fill in" the mask. we then feed the generated sentence, alongside the original claim, to a verification classifier model that classifies whether the claim is supported, refuted, or the information is insufficient to make a call. the rest of the paper is organized as such: section 2 gives an overview of the problem space. section 3 describes our preliminary experiments. sections 4 and 5 highlights our main methods (i.e. end-to-end model, experimental setup), and 6 reports our main results. sections 7 and 8 conclude our paper with a discussion and future works. dataset we use fever @xcite , a large-scale fact-checking dataset with around 5.4m wikipedia documents. claims were generated by extracting sentences from wikipedia (with possible mutations), and were annotated by humans with their verification label and/or evidence sentences from wikipedia. traditional pipeline traditional fact-checking systems (fig. 1a ) access knowledge within an external knowledge base (i.e. wikipedia) to validate a claim. they use a multi-step, pipelined approach, which involve ir-modules, such as document retrievers and evidence selectors, for retrieving the appropriate evidence, and verification modules that take in {claim, [evidences]} pairs and predict a final verification label our pipeline as shown in fig. 1b , our proposed pipeline replaces both the external knowledge base as well as the ir modules with a pretrained language model. in the remainder of this paper, we utilize bert. future work can explore other language models. ## exploratory experiments in order to determine the feasibility of our approach, we began by conducting a human review study on 50 random-selected claims from fever @xcite . participants were asked to validate each claim with only a language model, by following these steps: in this example, the user is verifying which university was founded by thomas jefferson. note that the user could alternatively choose to mask thomas jefferson in order to verify the founder of university of virginia. top-1 predicted @xmath0. in other words, we asked participants to serve as the "masking" and "verification classifier" components of our fact-checking pipeline in fig. 1b . two participants examined the 50 claims, and eventually achieved an average accuracy of 55%. 1 we also conducted this zero-shot study on a larger scale and in a more systematic way, by taking all claims in the full fever dataset, and always masking the last token. 2 otherwise, we preserve steps 2 and 3 from above. even with this naïve token-matching approach, we were able to obtain precision 56% and f1 59% for the positive label (support). our preliminary experiments' results illustrate that, with a good masking mechanism and verification model, language models can indeed feasibly be used for fact-checking. ## end-to-end fact-checking model enlightened by results from our preliminary experiments, we devise an end-to-end model that automates and improve upon the masking and verification steps that were conducted by humans. specifically, we resolve two limitations: 1. manual masking of claims, and 2. naïve validation of the predicted token that fails to deal with synonyms and other semantic variants of the answer. ## results and discussion the results of the three models are reported in on the other hand, we find that bert askb lags behind bert f inetune , as expected, on most metrics. we hypothesize this is due to the high capacity of the model, in comparison, and to the effectiveness of bert models in text classification. upon examining the results of these two models closely, we find that bert askb struggles mightily with the nei category (f1 score of 0.24 vs 0.53) indicating that our current approach might need specific modules to better tackle that category. as both models seem to be equally adept in identifying the support class (0.57 vs 0.59 f1), indicating that bert askb is unable to distinguish between refute and nei classes. future work can further investigate techniques to identify these two categories. interestingly, the bert f reeze achieves an accuracy score of 38% which is slightly better than a random baseline which achieves 33%. ## analysis of token prediction results in this section, we provide some examples of tokens predicted from bert to understand the performance of "evidence generation". first two examples in for example, sentence c from one of the the main insight we get from these analysis is that, the way the language model is initially pre-trained, greatly determines the way it should be "queried". ## conclusions & future work in this paper, we explored a new fact-checking pipeline that use language models as knowledge bases. unlike previous pipelines that required dedicated components for document retrieval and sentence scoring, our approach simply translates a given claim into a fill-in-the-blank type query and relies on a bert language model to generate the "evidence". our experiment shows that this approach is comparable to the standard baselines on the fever dataset, though not enough to beat the state-of-the-art using the traditional pipeline. however, we believe our approach has strong potential for improvement, and future work can explore using stronger models for generating evidences, or improving the way how we mask claims. in the future, we will investigate sequence-tosequence language models such as bart @xcite or t5 @xcite , that have recently shown to be effective on generative questionanswering @xcite . similarly, our proposed approach seem to struggle with correctly identifying nei cases, and we plan to investigate adding specific modules to deal with nei. lastly, we plan to explore new ways of pre-training language models to better store and encode knowledge.