The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models (2023)

Abstract: Large Language Models (LLMs) make natural interfaces to factual knowledge, but their usefulness is limited by their tendency to deliver inconsistent answers to semantically equivalent questions. For example, a model might supply the answer “Edinburgh” to “Anne Redpath passed away in X.” and “London” to “Anne Redpath’s life ended in X.” In this work, we identify potential causes of inconsistency and evaluate the effectiveness of two mitigation strategies: up-scaling and augmenting the LM with a passage retrieval database. Our results on the LLaMA and Atlas models show that both strategies reduce inconsistency but that retrieval augmentation is considerably more efficient. We further consider and disentangle the consistency contributions of different components of Atlas. For all LMs evaluated we find that syntactical form and task artifacts impact consistency. Taken together, our results provide a better understanding of the factors affecting the factual consistency of language models.

## introduction we have recently observed the development of several highly performant pretrained large language models (llms) that have expanded the boundaries of what we can expect from foundational language models. these recent successes have highlighted the potential of using language models as a simpler interface to factual knowledge @xcite . however, in fact critical settings we require not only high accuracy but also consistency, i.e. robustness to lexical variations in semantically equivalent queries. recent lm developments have mainly improved on accuracy, while the question of consistency has seen less attention. as exemplified in figure 1 , even sota lms may produce different outputs depending on lexical variations in semantically equivalent queries @xcite @xcite . this sug-fact anne redpath -place of death -edinburgh queries 1. anne redpath expired @xmath0. problems with inconsistency extend beyond factrelated accuracy, as they also indicate a tendency to hallucinate and a lack of factuality in foundational lms @xcite . as inconsistent models may be fragile in unexpected manners, they are difficult to interpret and we are in a worse position for providing limitations on what they may generate @xcite . to promote desirable properties such as robustness and interpretability we may need to reconsider our model designs. as an alternative to up-scaling, we have seen the rise of model designs guided by inductive biases to promote various properties @xcite . examples of such models are text retrieval-augmented models that condition predictions on retrieved text passages for improved adaptability, interpretability and efficiency @xcite @xcite , neurosymbolic models that condition predic-tions on logic for reasoning capabilities @xcite and fact-injected lms for interpretability @xcite . our goal in this paper is to improve our understanding of consistency in foundational lms. we start by investigating methods to improve model consistency. we consider two potential solutions, 1) model up-scaling and 2) wikipedia text retrieval augmentation, building on suitable inductive biases related to factual consistency. furthermore, we identify and investigate potential sources of inconsistency of lms. our contributions can be summarized as follows: ## data we describe the original pararel benchmark in section 2.1 and our improvements in section 2.2. section 2.3 explains the metrics studied for pararel*. ## effect of scaling and retrieval augmentation to investigate the effect of scaling and retrieval augmentation on the consistency of lms we evaluate llama (section 3.1) and atlas (section 3.2) on pararel* and report the results in section 3.3. ## effect of evaluation task format apart from retrieval-augmentation and scaling, we also expect form and evaluation task format to have had an impact on the consistency results in ## effect of retrieval components we found atlas to be more consistent compared to lms without retrieval augmentation. in this section we perform a deeper analysis on the effects of letting a model retrieve over some corpus with focus on the retriever and reader components. we use the dag depicted in figure 3 as a starting point for our reasoning. we define a query to be the combination of a fact tuple, f and template t , as depicted in figure 1 . we can see f and t as the semantics and syntax of a query respectively. a model prediction for a certain query and retrieved information can thus be described as o(t, f, r(f, t |β)|θ), where θ and β are model parameters of the reader and retriever respectively. the motivation for why retrieval-augmented lms should be more consistent than standard lms builds on two parts related to the retrieval and inference steps of the retrieval-augmented model. first, a perfect retrieval result should ideally be independent of the syntax (template) of a query conditioned on the semantics (facts) of the query. the retriever is trained on the more narrow task of identifying relevant factual aspects of a query and matching those to the information in the retrieval corpus, thereby becoming less dependent on syntactic variations. furthermore, the retriever has a more restricted output space and can only choose between already existing facts rather than create new ones. expres-sion (1) summarizes the expected behavior of an ideal retriever. ## related work while there is a large body of work on the factual accuracy of lms, the question of consistency has received less attention. this issue was investigated by @xcite , which introduced the pararel benchmark which forms the basis of our work here. on the accuracy side, there have been investigations that relate effects of the training data (primarily how frequently facts occur) to the correctness of factual predictions @xcite @xcite . the approach of viewing a complex lm as a causal system and carrying out interventions to investigate the effect of components on the model's behavior was pioneered by @xcite . further work by @xcite applies this methodology to examining fact associations and proposes a hypothesis of how models recall factual information stored in their parameters. ## conclusions and future work in this work, we investigate methods to improve factual consistency of lms and further reason about potential sources of inconsistency. we find that up-scaling, represented by llama, generally improves on consistency, while it is inefficient and surpassed by retrieval augmentation, represented by atlas. retrieval augmentation produce the largest consistency improvement. however, atlas does not achieve perfect consistency even for perfectly consistent retrieved passages, this indicates that additional inconsistency sources are involved and persistent in spite of consistent conditioning. these are potentially related to form anomalies and changeable reader dependency on the retrieved information. in conclusion, we have identified and measured several potential sources of (in)consistency that should aid future work on improving the robustness of lms, while much remains to be investigated. future work includes evaluating consistency of additional model designs, such as contextaugmented llms and models that build on knowledge graphs @xcite . we could also make use of methods similar to the ones proposed by @xcite to further investigate parametric versus non-parametric memorization in the context of retrieval augmentation and its effects on consistency.