Knowledge Corpus Error in Question Answering (2023)

Abstract: Recent works in open-domain question answering (QA) have explored generating context passages from large language models (LLMs), replacing the traditional retrieval step in the QA pipeline. However, it is not well understood why generated passages can be more effective than retrieved ones. This study revisits the conventional formulation of QA and introduces the concept of \textit{knowledge corpus error} . This error arises when the knowledge corpus used for retrieval is only a subset of the entire string space, potentially excluding more helpful passages that exist outside the corpus. LLMs may mitigate this shortcoming by generating passages in a larger space. We come up with an experiment of paraphrasing human-annotated gold context using LLMs to observe knowledge corpus error empirically. Our results across three QA benchmarks reveal an increased performance (10% - 13%) when using paraphrased passage, indicating a signal for the existence of knowledge corpus error.

## introduction large language models (llms) generate surprisingly fluent and informative texts. this led to many works utilizing the text data generated by these models for purposes such as instruction tuning @xcite and improving reasoning capability @xcite @xcite . open-domain question answering (qa) @xcite is a task where retrieving relevant passages from a corpus of factual information such as wikipedia is standard practice. recent works have attempted to generate such passages from llms, replacing the retrieval step of the traditional pipeline @xcite . despite their success, it is not well understood why these generated passages could be more effective than retrieved passages. these recent advancements lack robust links to prior research in qa posing a challenge to a holistic understanding. by revisiting the formulation of answer generation with retrieved passages @xcite @xcite , we identify the then-overlooked gap, which has become significant now due to the advance in llms @xcite . our discussion starts with the observation that the knowledge corpus from which the passages are retrieved is only a subset of the possible string space. more helpful passages to the reader may exist outside the knowledge corpus. unfortunately, retrieval, by definition, cannot utilize passages outside the knowledge corpus, potentially causing a shortfall. we refer to this as knowledge corpus error. in contrast, llms can generate passages from the entire string space, which may mitigate the inherent limits of retrieval. we empirically demonstrate the presence of knowledge corpus error where a passage from outside of wikipedia outperforms the humanannotated gold passage inside wikipedia in question answerin. we design an experiment of paraphrasing human-annotated gold context with llms. experiments with four qa benchmarks, nq @xcite , hotpotqa @xcite , strategyqa @xcite , and qasc @xcite result in 10% -13% gain in reader performance across three benchmarks, when using paraphrased passages. this gain supports our hypothesis that there exist more helpful passages than the gold passage outside the knowledge corpus. ## analytic discussion our task formulation follows retrieval augmented models for qa @xcite @xcite . these works view contexts as a latent variable for the qa model @xcite . ## empirical observation to observe knowledge corpus error, we study the effect of paraphrasing human-annotated gold contexts from qa dataset. gold context c @xmath0. while human annotation may be imperfect, we assume that this c gold acts as the best available passage from the knowledge corpus z, i.e., there is no retrieval error. in our experiment, c gold is paraphrased into c paraph , by prompting llms with c gold and q. then, c gold and c paraph are separately fed into the reader to compare the performance. as c gold is the best available context, any gains from paraphrasing should be attributed to reduced knowledge corpus error. ## conclusion in this work, we demonstrate that generated contexts may be more helpful than retrieved contexts in open-domain question answering. by revisiting the formulation of question answering, we identify a gap where retriever inevitably ignores potentially helpful contexts outside of the corpus. we call this knowledge corpus error, and design an experiment in order to observe knowledge corpus error empirically. paraphrasing the human-annotated gold contexts with llms led to increased reader performance in 3 out of 4 qa benchmarks, implying the existence of knowledge corpus error. the second limitation of this work is that it did not address the practical way to marry retrieval and generation via llms. regardless of the seeming benefit of context generation, this approach suffers from issues such as information staleness and hallucination. contemporaneous works explore various methods to leverage benefits of both retrieval and generation @xcite @xcite . this work is primarily concerned with the analytic understanding of how generation may have advantages over retrieval. we believe our work can inspire future contributions on empirical method of incorporating retrieval and generation. the third limitation of this work is that its scope was limited to question answering. conditioning generation on retrieved context is a well-studied approach in language modeling @xcite @xcite . it will be worth exploring how knowledge corpus error manifests within language modeling. a dataset nq: we use the kilt @xcite version of nq 4 . we use the dev split after excluding the instances where context does not include answers, which results in 2532 samples. hotpotqa: we use the dataset from its original source foot_3 . we use the dev split, which includes 7405 samples. to reduce the inference cost, we only use the subset of first 1531 samples. strategyqa: we use the dataset from its original source foot_4 . we use the training split, which includes 2290 samples. we use the training split because the dev split contained too few ( 490 ) examples. qasc: we use the dataset from its original source foot_5 . we use the dev split, which includes 926 samples.