Detrimental Contexts in Open-Domain Question Answering (2023)

Abstract: For knowledge intensive NLP tasks, it has been widely accepted that accessing more information is a contributing factor to improvements in the model’s end-to-end performance. However, counter-intuitively, too much context can have a negative impact on the model when evaluated on common question answering (QA) datasets. In this paper, we analyze how passages can have a detrimental effect on retrieve-then-read architectures used in question answering. Our empirical evidence indicates that the current read architecture does not fully leverage the retrieved passages and significantly degrades its performance when using the whole passages compared to utilizing subsets of them. Our findings demonstrate that model accuracy can be improved by 10% on two popular QA datasets by filtering out detrimental passages. Additionally, these outcomes are attained by utilizing existing retrieval methods without further training or data. We further highlight the challenges associated with identifying the detrimental passages. First, even with the correct context, the model can make an incorrect prediction, posing a challenge in determining which passages are most influential. Second, evaluation typically considers lexical matching, which is not robust to variations of correct answers. Despite these limitations, our experimental results underscore the pivotal role of identifying and removing these detrimental passages for the context-efficient retrieve-then-read pipeline.

## introduction knowledge-intensive nlp tasks such as opendomain question answering @xcite and evidence-based fact verification @xcite require models to use external sources of textual information to condition answer generation in response to an input. this family of tasks shares a 1 code and data are available on https://github.com/ xfactlab/emnlp2023-damaging-retrieval common architecture for modelling @xcite @xcite @xcite where a two-stage architecture of a retriever model finds contextual passages and passes these to a reader module that uses this context when it generates an answer. in this two-step pipeline, expected performance (such as answer exact match score) increases monotonically as more information is provided to the reader model at test time. therefore, it is commonly conceived that providing the reader with more context improves overall performance (izacard and grave, 2020; @xcite . however, empirical studies @xcite contradict this general belief: even when true positive passages are provided to the model, the presence of relevant but non-evidential documents can change correct outputs to incorrect ones in a phenomena called damaging retrieval. while this phenomena was not thoroughly explored in the context of question answering, it could suggest that the current reader may be influenced by damaging retrieval, potentially leading to a decline in performance. in this paper, we propose an approach to address-ing this problem by identifying and removing contexts that are detrimental to the performance of the model. as described in figure 1 , we treat the reader model, fid @xcite , as a blackbox oracle and identify which passages are damaging by evaluating how different subsets of the retrieved context cause the answer to change. our experiments are conducted on two question answering tasks: natural questions @xcite and triviaqa @xcite , evaluating the effect of three ir systems: dpr @xcite , seal @xcite , and contriever @xcite . our findings challenge the conventional assumption that more passages lead to higher performance. in fact, introducing additional relevant contexts can actually worsen the performance of the model in question answering. by excluding passages that have a detrimental effect, we observe up to 10% improvement in the exact match score under ideal conditions without requiring any architectural modifications. notably, these gains surpass the performance improvements seen in recent works that propose fid extensions like fid-light @xcite and fie @xcite . in addition, we analyze why automatically identifying damaging contexts is challenging, even with attentionbased and model-based proxies for filtering. despite these inherent limitations, considering effectiveness and operational efficiency, our empirical findings could potentially direct the focus of opendomain nlp models and yield performance improvements when using fewer context passages. ## motivating pilot study we demonstrate the effect of damaging passages extending @xcite for the fid model demonstrating that the model performance is sensitive to irrelevant context information. to select damaging context passages, we employ two methods: random sampling and negative sampling. for random sampling, we select passages from the corpus, c, uniformly at random, while for negative sampling, we use bm25 to select passages that do not contain the correct answer but have high lexical overlaps. results are reported in figure 2 an ## damaging passages from retrievers section 3 simulates the impact of damaging passages. however, these negative passages are sampled from a distribution different from what the reader is trained on. therefore, we experiment with retrieval models to confirm the presence of damaging effects in the retrieve-then-read pipeline. while retrievers predict a set of passages that are relevant to the model, it does not have perfect precision, and we do not have knowledge of which passages from the retriever are beneficial or detrimental. in order to identify wheter a passage is damaging or not, we utilize the reader model as a black box oracle to distinguish damaging contexts from positive contexts. starting with the top-1 passage, we incrementally add passages from the retrieved list and evaluate how the answer changes. if the inference is correct with the top-1, it is classified as a positive context. we then proceed to evaluate the top-1 and top-2 passages together. if those passages generate an incorrect answer, the second evidence passage will be considered damaging. this is possible because of the order invariance property of fid. therefore, the process can be repeated iteratively for up to the top-n passages. we then use this sequence of predictions to yield the exact match pattern. the exact match pattern is a binary sequence where a 1 represents that the kth prediction matches the answer, and 0 otherwise (figure 1 ). this string represents an exact match at k (em@k) when using up to top-k passages as input. from this, we also compute accumulated exact match (acem). acem@k is the maximum of all em@k values up to k. for instance, acem@k equals 1 if at least one of the values in (em@1, em@2, ..., em@k) equals 1. thereby, the discrepancy between acem@k and em@k can be used to measure the damaging effects on retrievers. we utilize three different retrievers for two question answering validation datasets, natural questions @xcite and triviaqa @xcite , which were evaluated on fid @xcite . following previous work, we use a corpus of non-overlapping 100 word chunks for retrieval. we use the top-100 candidate passages retrieved by dpr foot_1 @xcite , seal foot_2 @xcite , and contriever @xcite foot_3 . for dpr, we utilize the published retrieval result, while for the other retrievers, we run the code using the default parameters. we use two published models of fidlarge that were trained on dpr retrieved contexts from nq/triviaqa for inference. results are illustrated in figure 3 . all models show higher acem@k on all k values. for nq, the em@100 scores from dpr, seal, and contriever are 52.5%, 50.0%, and 50.7% while attaining 62.3%, 59.4% and 60.8% in acem@100. this indicates the existence of damaging effects in retrieved contexts, and the model performance could be at least 9% points higher if the retrieved passages can be refined to remove damaging passages. ## experiments 6.1 probe-based selection inference we evaluate all 6 probing strategies from section 5.2. initially we add padding passages and fix the context size to 100 during the inference. for probe 3, showing the highest em score, we further experiment with varying context sizes of 5, 10, 20 and 40 on each dpr-retrieved test set. the remaining experimental setup follows section 4. ## conclusions the reader models in retrieve-then-read pipelines are sensitive to the retrieved contexts when generating answers. damaging passages in this set can lead to incorrect responses. filtering damaging passages results in increases in em scores without the need for architectural modifications. despite shortcomings in evaluating qa with exact match, we demonstrate that by filtering passages, models can achieve 10% higher em scores using subsets of context that are 20x times smaller.