Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models (2024)

Abstract: Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs). Our experiments reveal that this example-level performance inconsistency exists not only between retrieval-augmented and retrieval-free LM but also among different retrievers. To understand this phenomenon, we investigate the degeneration behavior of RALMs and theoretically decompose it into four categories. Further analysis based on our decomposition reveals that the innate difference in knowledge sources and the unpredictable degeneration of the reader model contribute most to the inconsistency. Drawing from our analysis, we introduce Ensemble of Retrievers (EoR), a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. Our experiments on Open Domain Question Answering show that EoR substantially improves performance over the RALM with a single retriever by considerably reducing inconsistent behaviors.

## introduction although large language models (llms) have shown their superiority in many nlp tasks @xcite , they are known to struggle with factual hallucinations @xcite and outdated parametric knowledge @xcite . retrieval-augmented language models (ralms) as an ad-hoc technique have been proven to effectively alleviate these problems @xcite . in most ralm systems, a retriever takes the responsibility to retrieve relevant information from some external knowledge sources (e.g., wikipedia dump @xcite , search each cell's number represents the proportion of questions answered incorrectly by the column retriever that was correctly answered by the row retriever. 0 represents all questions correctly answered by the row retriever can be correctly answered by the column retriever, which implies the column retriever consistently outperform the row retriever. see equation 1 for formal definition. although ralms show their effectiveness at the corpus level @xcite , retrievalaugmentation does not consistently promote the original retrieval-free lm but sometimes hurts its performance at the individual example level @xcite @xcite . in this work, we observe that variability in example-level performance exists not only between 1 4833 retrieval-augmented and retrieval-free lms, but actually among different retrievers 2 (e.g. figure 1 ). we call this observed phenomenon as retriever inconsistency. specifically, we use open-domain question answering (odqa; @xcite as our benchmark task and build 15 different retrievers by retrieving from different knowledge sources (search engine, wikipedia dump and parametric knowledge) and implementing diverse processing methods @xcite . our experiment reveals that, on average, more than 16% of questions incorrectly answered by one retriever can be corrected by an alternative retriever, and this result holds for all testing models and datasets. to further investigate the reasons behind retriever inconsistency, we theoretically show that ralm's degenerate behaviors on odqa under regular conditions can be divided into four categories: retriever error, extraction error, hallucination error, and lucky guess. we further empirically investigate the example-level error occurrence behaviors of these four types of errors and the results indicate a ubiquitous inconsistent pattern across retrievers for every error type, which collectively contributes to the retriever inconsistency. our further analysis reveals that the innate difference in knowledge sources, such as the absence of post-2018 information in the 2018 wiki dump or search engine's deficiency in understanding tricky queries, and the inevitable and unpredictable degenerated behavior of the reader model, such as hallucination or the weak robustness to irrelevant context, serve as the main reason of retriever inconsistency. inspired by our analysis, we propose ensemble of retrievers (eor), a trainable framework that first samples from ralm with different retrievers and then rejects based on a voting mechanism that measures similarity between answers. not only can eor reduce retriever errors by adaptively retrieving from the most appropriate knowledge source, but effectively reduce errors caused by unpredictable degradation of the reader model by comparing answers from different retrievers, based on our observation of inconsistent model error behavior and 2 we distinguish different retrievers by their knowledge sources and text processing methods. we consider two retrievers equal if and only if their retrieved text chunks are exactly the same for the same query. retrieval-free could be thought of a singular retriever which reads the query and outputs an empty set. the intuition that incorrect answers vary while correct answers are always similar. by introducing controlling parameters in our framework, we can easily construct an optimization problem which can be solved by a heuristic search algorithm, and automatically search for the optimal retriever pool used for sampling. our framework is compatible with any llm and does not require any training on them. experiment shows that eor can effectively improve the performance consistency compared to ralm with a single retriever, thereby improving the corpus performance on odqa. ## retrievers are inconsistent to investigate the example-level inconsistent behavior across retrievers and the reasons behind it, we adopt the single-hot short-form odqa task, which consists of factual questions with short and clear answers, as our benchmark task. the straightforward task format and reliable automatic evaluation metrics @xcite enable us to quickly and accurately evaluate the correctness of model responses and retrieved documents, facilitating indepth theoretical and empirical analysis. ## ensemble of retrievers our analysis provides a strong rationale for adopting an ensemble of retrievers, which can retrieve from different sources, and leverage the irregular error behavior to reduce the degeneration of the reader model. in fact, we can calculate the theoretical upper bound of the ensemble of retrievers by assuming a perfect voting mechanism that will always select the correct answer as long as one retriever outputs the correct one, see figure 5 . it demonstrates an obvious monotonic increasing trend implying the great potential of the ensemble of retrievers. however, the variability in performance within models with the same retriever pool size underscores the importance of understanding both what to include in the ensemble and how to effectively combine them. ## conclusion this work focus on ralm's performance inconsistency across different retrievers. our results show that retriever inconsistency is ubiquitous across different retrievers and base models. to investigate the reasons behind it, we theoretically decompose ralm's failure into four categories, which serve as a basis for analyzing the degeneration behavior of ralm. our experiments based on our decomposition reveal that innate differences in knowledge sources and the unpredictable degeneration of the reader model are the main causes for the inconsistency behavior. we further propose en-8 semble of retrievers (eor), a trainable framework compatible with any llms and retrievers. our experiments demonstrate that eor can effectively boost ralm's performance by adaptively retrieving from multiple knowledge sources and reducing irregular errors made by the reader model.