Carpe diem: On the Evaluation of World Knowledge in Lifelong Language Models (2024)

Abstract: The dynamic nature of knowledge in an ever-changing world presents challenges for language models trained on static data; the model in the real world often requires not only acquiring new knowledge but also overwriting outdated information into updated ones. To study the ability of language models for these time-dependent dynamics in human language, we introduce a novel task, EvolvingQA, a temporally evolving question-answering benchmark designed for training and evaluating LMs on an evolving Wikipedia database. The construction of EvolvingQA is automated with our pipeline using large language models. We uncover that existing continual learning baselines suffer from updating and removing outdated knowledge. Our analysis suggests that models fail to rectify knowledge due to small weight gradients. In addition, we elucidate that language models particularly struggle to reflect the change of numerical or temporal information. Our work aims to model the dynamic nature of real-world information, suggesting faithful evaluations of the evolution-adaptability of language models. Our data construction code and dataset files are available at https://github.com/kimyuji/EvolvingQA_benchmark.

## introduction large language models (llms) @xcite @xcite have demonstrated remarkable capabilities in encoding vast amounts of knowledge in massive training data, which can be applied for downstream tasks such as knowledgeintensive question-answering and multi-hop reasoning. however, knowledge is not static: scientific discoveries, cultural trends, and linguistic creativity are constantly updated and edited as the world * carpe diem is a latin phrase that translates to "live in the present" in english. it encourages individuals to make the most of the present moment. meanwhile, previous research has shown that language models pre-trained on reliable knowledge sources such as wikipedia can substitute knowledge bases by storing knowledge in their parameters and be applied to various downstream tasks @xcite . to keep these models up to date with evolving world knowledge, it is desirable to apply continuous pretraining rather than periodically re-training from scratch. continued learning of existing models over sequential time-varying data remains one of the critical challenges in machine learning and has been attribute evolvingqa ckl temporalwiki streamingqa realtimeqa (ours) @xcite @xcite widely discussed in previous literature, often referred to as continual learning (cl) @xcite @xcite or lifelong learning. this learning paradigm addresses the problem of learning on multiple tasks/data sequentially, assuming that the data from the previous session is inaccessible when starting the next training session. the primary goal is to preserve previously acquired knowledge while learning new concepts. however, in real-world scenarios, consistent accumulation of world knowledge while forgetting outdated knowledge is desirable due to changes in world knowledge. models are required not only to learn new information but also to forget or update outdated information 1 . for example, the knowledge from 2017 that "donald trump is the president of the us." became outdated in 2021, when it was substituted by the updated knowledge "joe biden is the president of the us." several benchmarks have been introduced to evaluate language models on temporally changing knowledge @xcite @xcite @xcite . however, these fall short in providing holistic evaluations of knowledge preservation and modification in the context of real-world applications. while @xcite address both changed and unchanged knowledge, models are evaluated using template-based knowledge probing (i.e., lama task @xcite ), which may not represent applicability in the real world. @xcite focus on evaluating new and updated knowledge, thereby failing to assess catastrophic forgetting of previous knowledge after updated knowledge acquisition. we provide a comprehensive comparison of benchmarks in our goal is to create a benchmark for holistic evaluation of the temporal adaptation capabilities of language models. we propose evolvingqa, a novel benchmark for pre-training and evaluating 1 new knowledge refers to added knowledge which was previously nonexistent, while updated knowledge refers to added knowledge which invalidates previous knowledge. lms over evolving wikipedia data. we propose an automated pipeline to construct our benchmark using llms, which allows us to extend our benchmark to many time steps and to easily update the benchmark into the future, as depicted in figure 1 . we use the question-answering (qa) task for downstream evaluation to measure continual learning that translates into real-world applicability. we find that continual pre-training baselines (1) suffer from catastrophic forgetting and (2) fail to forget outdated knowledge (3) or incorporate updated knowledge, highlighting the relevance of our benchmark. we provide comprehensive analyses on why and how such circumstances occur. our contributions are as follows: ## evolvingqa in this section, we introduce evolvingqa, a novel benchmark for evaluating lm's ability to forget and update dynamically evolving knowledge. evolv-ingqa is divided into continual pre-training corpora and evaluation dataset. for continual pretraining corpora, we collect consecutive wikipedia snapshots and conduct heuristic filtering. for evaluation dataset, we collect a qa dataset through automatic generation and validation using llm. since both pre-training and evaluation data can be collected automatically, evolvingqa could be extended to future time steps. ## conclusion our research shed light on the importance for lms capability of dynamically accumulating and revising information to reflect the continual evolution of world knowledge, which were under-explored in previous studies. our proposed evolvingqa benchmark includes evaluation for the adaptability of llms to such continual changes, revealing significant deficiencies in current models' abilities to forget and update outdated knowledge, especially in numerical and temporal data. our findings show that this is due to the ineffectiveness of gradient update in managing updated knowledge. we hope that our work acts as a cornerstone for future research aiming to bridge the existing gaps in llms' temporal adaptation capabilities. limitation our study's limitations include the evolvingqa dataset's lack of real-time updates. as wikipedia updates monthly, there's a gap between current events and their reflection in the dataset. additionally, using a single llm for dataset construction and filtering processes introduce noise. llms can hallucinate and generate inaccurate data, and validation using the same llm may not be possible to completely eliminate such risks. though usage of advanced models such as gpt-4 and different validation model may mitigate this, it remains a concern. furthermore, the overall performance is low, since closed-book qa itself is a very challenging task, and this can be alleviated by training models with larger capacities @xcite . finally, our framework do not allow control over question difficulty, affecting the evaluation results depending on the complexity of the questions. this might be addressed with refined prompting or additional pre-processing.