Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization (2024)

Abstract: Open-domain Question Answering (OpenQA) aims at answering factual questions with an external large-scale knowledge corpus. However, real-world knowledge is not static; it updates and evolves continually. Such a dynamic characteristic of knowledge poses a vital challenge for these models, as the trained models need to constantly adapt to the latest information to make sure that the answers remain accurate. In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains. In this paper, we investigate the generalization performance of a retrieval-augmented QA model in two specific scenarios: 1) adapting to updated versions of the same knowledge corpus; 2) switching to completely different knowledge domains. We observe that the generalization challenges of OpenQA models stem from the reader’s over-reliance on memorizing the knowledge from the external corpus, which hinders the model from generalizing to a new knowledge corpus. We introduce Corpus-Invariant Tuning (CIT), a simple but effective training strategy, to mitigate the knowledge over-memorization by controlling the likelihood of retrieved contexts during training. Extensive experimental results on multiple OpenQA benchmarks show that CIT achieves significantly better generalizability without compromising the model’s performance in its original corpus and domain.

## introduction open-domain question answering (openqa) @xcite aims at answering factual questions using a large-scale external knowledge corpus. this is in contrast to closed-book question answering @xcite wherein the model is expected to directly answer questions with no access to external knowledge. in general, closed-book qa optimizes for memorization of knowledge in model parameters, while openqa focuses on retrieving relevant knowledge from an external corpus. openqa typically employs a retrievalaugmented approach @xcite @xcite , involving a two-stage process: a retriever to select relevant documents, followed by a reader to derive answers from these documents. it is more practical for real-world applications as it enables the use of extensive and varied knowledge sources for answering questions. retrieval-augmented openqa models rely on an external corpus to physically store the knowledge. however, real-world knowledge is not static; it updates and evolves continually. therefore, it is essential to build models that are able to use fresh and real-time knowledge @xcite , but the dynamic characteristic of knowledge poses a vital challenge as the trained models need to constantly adapt to the latest information to make sure that the answers remain relevant and accurate. in addition, closed-book qa systems have been proved limited in adapting to new information or domains due to their reliance on pre-existing knowledge, and updating their parametric knowledge requires extensive large-scale pre-training. nevertheless, it is still unclear how well openqa systems can transfer to leveraging unseen corpora and domains during training. in this paper, we first investigate how well stateof-the-art retrieval-augmented models, such as atlas @xcite , can adapt to new and diverse knowledge corpora. specifically, we explore the model's performance in two scenarios: 1) adapting to updated versions of the same corpus (in §2.2.1); 2) switching to completely different domains (in §2.2.2). our investigation involves three settings: directly applying a pre-trained model, finetuning the model with the new corpora, and training the model afresh on the new corpora. initial experi-ments reveal that the model faces challenges in both scenarios. when directly transitioning to an updated corpus, there is a noticeable performance decline. even additional tuning on the newer version doesn't achieve the same effectiveness as training from scratch with the new data (56.9→59.5 vs 62.2 in we hypothesize and validate that such generalization challenges stem from the reader's overreliance on memorizing the knowledge retrieved from the external corpus. this reliance primarily arises as the reader, with its primary training objective optimized for qa accuracy, often opts to hardcode a substantial amount of retrieved knowledge into its parametric memory. such kind of overmemorization reduces the reader's dependency on the retriever to choose more relevant contexts. this phenomenon hampers the model's generalizability, particularly to updates in the knowledge corpus or changes in the knowledge domain. for instance, given a question who is the prime minister of the uk?, if a model has already hard-coded an outdated answer boris johnson into its parameters (while being trained on an old corpus), it is harder to change its response even if the new information rishi sunak from an updated corpus is available. to address this issue, we introduce corpus-invariant tuning (cit), a simple but effective training strategy to improve the corpus generalizability of retrieval-augmented text generation models. cit aims to mitigate the reader's tendency to memorize the documents retrieved from the corpus during training. this pushes the reader to rely more on retrieved documents to answer the input questions, rather than relying on memorizing the knowledge facts into its parameters. to achieve this, we propose a novel loss term to prevent memorization during training by controlling the likelihood of the retrieved documents. through extensive experiments across various openqa benchmarks @xcite @xcite , carried out in both zero-shot and continual finetuning scenarios, we demonstrate that a retrievalaugmented model trained using our proposed cit loss exhibits considerably enhanced generalizability across different corpora. this is evident by the considerable improvements in exact match (em) scores, reaching up to a 2.1% absolute gain. our contributions can be summarized as follows: ## corpus-invariant tuning motivated by the observed limitations in the generalization capabilities of retrieval-augmented models, we introduce corpus-invariant tuning (cit) to mitigate memorizing the lexical content of retrieved documents. specifically, we posit that the generalization difficulties encountered by retrievalaugmented text generation models arises via excessive memorization the documents retrieved from the external corpus by the reader. in order to achieve higher question-answering accuracy during training, the reader tends to "hard-code" a large volume of retrieved documents rather than relying on an improved retriever for a better selection of relevant contexts, as is empirically validated in the document retrieval evaluations of section 4.5. this limits the model's ability to generalize because excessive memorization of documents by the reader dictates that when the external corpus is updated or transitions to a different domain, the model faces increased difficulty in adapting and correcting its knowledge compared to learning from scratch. 6 validation here we provide an empirical validation on our hypothesis that the degradation of model generalization ability is caused by overmemorization of retrieved knowledge. we replace the retrieved contexts with ground-truth retrieval results on wiki-2018, and conduct a stand-alone evaluation with the reader. we report both the em score and the overlap rate, i.e., the percentage of incorrectly predicted answers that have overlaps with the ground-truth retrieval results in wiki-2017. the results are shown in dataset training em overlap corpus score rate nq wiki-2017→2018 63.6 76.3 wiki-2018 65.2 30.2 triviaqa wiki-2017→2018 78.1 80.8 wiki-2018 78.7 41.0 ## conclusion in this paper, we present corpus-invariant tuning (cit), a simple but effective training strategy to improve the generalization ability of a retrievalaugmented text generation model across different corpus versions and different knowledge domains. the main idea of cit is to mitigate rigid knowledge memorization during training, so that the reader module can easily accept new knowledge in the retrieved documents and adapt to novel unseen domains. specifically, we control the reader's likelihood of the retrieved documents during training, to make sure that over-memorization of corpus knowledge is prevented. extensive experiments are conducted on multiple openqa datasets in both zeroshot and fully-supervised training settings, and the results demonstrate that training the model with the proposed cit loss significantly improves the model's generalizability across different corpus versions and knowledge domains, without sacrificing the model's inherent performance in its original domain.