M u L an: A Study of Fact Mutability in Language Models (2024)

Abstract: Facts are subject to contingencies and can be true or false in different circumstances. One such contingency is time, wherein some facts mutate over a given period, e.g., the president of a country or the winner of a championship. Trustworthy language models ideally identify mutable facts as such and process them accordingly. We create MuLan, a benchmark for evaluating the ability of English language models to anticipate time-contingency, covering both 1:1 and 1:N relations. We hypothesize that mutable facts are encoded differently than immutable ones, hence being easier to update. In a detailed evaluation of six popular large language models, we consistently find differences in the LLMs’ confidence, representations, and update behavior, depending on the mutability of a fact. Our findings should inform future work on the injection of and induction of time-contingent knowledge to/from LLMs.

## introduction pretrained and large language models (llms) trained on vast amounts of text are known to encode factual knowledge (petroni et al., 2019; jiang et al., 2020; liu et al., 2023). by using cloze-tests or next-token prediction, it is possible to retrieve facts memorized during pretraining (meng et al., 2022b; yin et al., 2022; chalkidis et al., 2023). however, factual knowledge changes over time, and facts such as who is the president of a country, or where someone lives, are time-contingent truths, drifting or mutating with the passage of time. dhingra et al. (2022) posit that pretrained llms are not time-aware, given that they do not predict the right answer for a specific time period. jain et al. (2023) and qiu et al. (2023) prompted llms to measure their ability to reason with temporal information, finding again that llms show limited time awareness and lag behind human performance. in this paper, we challenge this belief by studying the representations and behavior of llms with respect to facts that mutate over time, hypothesizing that despite the low performance encountered in the temporal tasks studied so far, llms do encode mutability in their knowledge representations. the inherent temporality of facts has also been studied from the perspective of model updates, exploring how to best edit the knowledge embedded in pretrained llms. most of the editing techniques have focused on modifying the parameters directly (de cao et al., 2021; mitchell et al., 2022; meng et al., 2022a), however cohen et al. (2023) found that a simple in-context editing obtains more consistent updates than direct parameter modifications. in this paper, we explore llms behavior with respect to mutability by studying the effectiveness of knowledge updates given a fact's mutability type. in order to study llms' ability to anticipate when facts are time-contingent, we introduce the mulan (mutability in language models) dataset. unlike lama (petroni et al., 2019), which contains mostly immutable relations, and tem-plama (dhingra et al., 2022), which contains exclusively mutable relations, mulan contains a balanced mix of both types of relations, curated to enable the controlled study of mutability, while also disentangling this phenomenon from cardinality, i.e., the property of some relations to take multiple objects at the same time. mulan contains 35 relations extracted from wikidata, foot_1 each with up to 1,500 queries. equipped with this resource, we address the following research questions. rq1: will llms exhibit lower confidence and performance on time-contingent truths? we find that there is a difference in performance, but the gap in confidence is even more impressive ( ## the mulan benchmark probing similar to petroni et al. (2019), we probe language models for their encoded knowledge using wikidata triples (subject, relation, object) e.g., (germany, capital, berlin). to probe language models, these triples can be formulated as a query for which, given a subject and a relation (a query; e.g., "the capital of germany is [x]."), the goal is to retrieve the corresponding object(s). naturally, some relations, e.g., capital of, are binary or one-to-one (1:1) foot_2 but many others may have many possible completions being n-ary (1:n), e.g., the relation shares borders with. both 1:1 and 1:n relations can be involved in timecontingent truths. foot_3 but time-contingent 1:1 facts are, in a sense, 1:n across time, so including 1:n facts that are not time-contingent provides for an interesting control scenario. therefore, we construct mulan to contain 3 sets of relations: immutable-1 (1:1), immutable-n (1:n), and mutable (1:n). note how instances of immutable-n and mutable relations might look similar to a language model from a training perspective, given that the data is shuffled at training time. @xcite dataset to create mulan , we first select the set of relations for each mutability type. we use lama (petroni et al., 2019) and tem-plama (dhingra et al., 2022) as a starting point and further expand the number of candidate relations with the wikidata database. we then verify the mutability and cardinality of a relation by using the average number of objects, defining a threshold to differentiate between immutable-1 and immutable-n, and validating that the cardinality of mutable and immutable-n is similar on average. by doing so, we obtain 12 immutable-1 relations, 10 immutable-n relations, and 13 mutable relations. all relations are listed in ## conclusion using our novel resource, mulan , we find new evidence for time awareness in llms, specifically for encoding of fact mutability in their representations and for the comparative ease of editing of mutable facts versus immutable ones. research on the learning dynamics of llms may further investigate how llms trained on shuffled data acquire such time awareness. from a practical standpoint, on the other hand, our findings should inform future work on the induction of knowledge from llms, and the design choices for updating llms.