Distribution Shifts Are Bottlenecks: Extensive Evaluation for Grounding Language Models to Knowledge Bases (2024)

Abstract: Grounding language models (LMs) to knowledge bases (KBs) helps to obtain rich and accurate facts. However, it remains challenging because of the enormous size, complex structure, and partial observability of KBs. One reason is that current benchmarks fail to reflect robustness challenges and fairly evaluate models.This paper analyzes whether these robustness challenges arise from distribution shifts, including environmental, linguistic, and modal aspects.This affects the ability of LMs to cope with unseen schema, adapt to language variations, and perform few-shot learning. Thus, the paper proposes extensive evaluation protocols and conducts experiments to demonstrate that, despite utilizing our proposed data augmentation method, both advanced small and large language models exhibit poor robustness in these aspects. We conclude that current LMs are too fragile to navigate in complex environments due to distribution shifts. This underscores the need for future research focusing on data collection, evaluation protocols, and learning paradigms.

## introduction language models (lms), such as bert @xcite , t5 @xcite , and the gpt series @xcite , have demonstrated impressive capabilities in understanding and generating languages, highlighting the potential for artificial general intelligence (agi). however, a major obstacle to achieving this goal is that lms mainly built on natural languages are not yet well-grounded to real-world environments, such as knowledge base (kb), an environment of enormous size, complex structure, and only partially observable to lm. though lms are highly skilled at natural language question answering (qa) today, the task of knowledge base question answering (kbqa) aims to parse natural language queries into formal queries on kbs, such as freebase @xcite and wikidata @xcite . the significance of this task lies in building language agents on complex environments @xcite , rather than merely recalling answers from the lm's stored knowledge. now, numerous lm-driven models @xcite continue to achieve higher f1/hits@1 scores on kbqa benchmarks. however, achieving higher scores does not necessarily guarantee the development of robust and dependable models. we still need to ask whether such improvements apply to extensive scenarios, as benchmarks almost always create questions via crowdsourcing and evaluate with simplistic metrics ( to achieve this goal, we need to grasp the key factors in robustness. for modern deep learning systems, the amount of training data could be extremely rich, but robustness is closely related to data distribution @xcite . in the general area of natural language processing (nlp), large-scale corpora have been collected and used for effective training @xcite . however, real-world environments are rarely so accommodating, e.g., large kbs contain complex structures and schema items, and building a large-scale and representative corpus is quite challenging. the problem of inconsistent data distribution during training and inference, i.e., distribution shifts as shown in figure 1 , may negatively impact the per-d-series machines was designed by which computer designer? (and cvg.computer_videogame (join (r cvg.cvg_designer.games_designed) m.0gvz6l)) @xcite designed m.04_79wm)) obama married michelle robinson, a lawyer who had also excelled at harvard law. ## related work compared to existing research, the necessity of this study is threefold. first, while lms are commonly evaluated on natural language tasks @xcite , the complexity increases significantly when these models are applied to environments like kbs, where data is structured rather than purely unstructured natural language @xcite . second, the evaluation protocols used in kbqa benchmarks tend to be uniform, leading to an insufficient evaluation of model robustness @xcite . finally, recent reviews in this field @xcite have largely overlooked the advancements in the development and application of lms, particularly llms. ## challenges from distribution shifts in this paper, the robustness of a kbqa model refers to its ability to adapt to various natural language inputs and maintain consistent performance when data distribution shifts. due to the distribution shifts between the training corpus of lms and kb environments, grounding lms face robustness challenges from environmental, linguistic, and modal aspects. ## evaluation protocols regarding these challenges, we introduce extensive protocols for evaluating lms in several aspects overlooked by current benchmarks. ## analysis we report and analyze the experimental results in this section for each aspect. ## conclusion despite the recent progress of lm-driven models, robustness challenges posed by distribution shifts for the kbqa task are rarely discussed. our analyses call for further research into better evaluation protocols for grounding lms to kbs and enhancing the robustness of environmental, linguistic, and modal aspects. notably, the experiments reveal that llms sometimes simply copy the provided prompt. it indicates that the existing methodologies for grounding llms are yet to prove their efficacy and superiority. future research issues include collecting more balanced environment-specific corpora and improving the llm learning paradigms. for the corpora, our experiments show that the data augmentation techniques deserve further research. limitations 1) for question generation, the verbalization process of the gain method relies heavily on largescale kbqa annotations. the training data influence the style of generated questions, and overly complex logical forms (e.g., with three or more hops) are difficult to convert into natural language questions. besides, synthetic data is less diverse and natural than human annotations, though it improves generalization performance. 2) multilingual kbqa is a problem that lacks attention in overall i.i.d. compositional zero-shot model on grailqa valid set em f1 em f1 em f1 em f1