Language Models Are Poor Learners of Directional Inference (2022)

Abstract: We examine LMs’ competence of directional predicate entailments by supervised fine-tuning with prompts. Our analysis shows that contrary to their apparent success on standard NLI, LMs show limited ability to learn such directional inference; moreover, existing datasets fail to test directionality, and/or are infested by artefacts that can be learnt as proxy for entailments, yielding over-optimistic results. In response, we present BoOQA (Boolean Open QA), a robust multi-lingual evaluation benchmark for directional predicate entailments, extrinsic to existing training sets. On BoOQA, we establish baselines and show evidence of existing LM-prompting models being incompetent directional entailment learners, in contrast to entailment graphs, however limited by sparsity.

## introduction pre-trained language models have shown impressive performance in natural language understanding (nlu), where prompting methods are widely used for fine-tuning @xcite @xcite . in this paper, we specifically investigate predicate entailment detection, an important sub-task of nlu and specifically, nli. the task is to predict, given that predicate p holds between @xmath0, b>, whether it can be inferred that predicate q also holds @xmath1, b>. for instance, "john shopped in ikea" entails "john went to ikea", but not "john drove to ikea". the primary distinction between predicate entailments and semantic similarity, apart from being focused on predicates, is that the former involves directional entailments as well as symmetric ones. directional entailments are those p ⊨ q (p entails q) where q ⊭ p; conversely, symmetric entailments are those p ⊨ q where q ⊨ p as well (namely p ≡ q). ## prompt fine-tuning lm with levyholt in this section, we test for lms' ability to learn directional entailments with the s&s prompt-based gauge model. we use roberta-base as the primary subject, as it is used by @xcite , and is sufficiently lightweight for experiments to run efficiently. in appendix c, we also report results on roberta-large and bert models for key experiments, where results are consistent. we use s&s subset to denote s&s model fine-tuned on each subset. experiments are graphically summarized in figure 1 . each edge denotes a levyholt subset made of the two sub-groups; the number on each edge is the auc norm that s&s achieves on each subset. for separating an entailed sub-group from a non-entailed one, the original labels are used; for separating two entailed or two non-entailed subgroups, the one with more similar predicates (more paraphrastic) is assigned label "1", the other "0". 4 note that we fit s&s to a number of different subsets, so we cannot simply re-use the original hyper-parameters. instead, to provide a level playing field, we follow @xcite in log-uniformly sampling 100 hyper-parameter sets from their specified ranges; for each subset, we choose the one with best dev set result. if a method is insensitive to directional entailments, then it would treat entailments as similarity between unordered pairs of predicates; it would model paraphrases, dirtrue and dirfalse similarly, where dirtrue and dirfalse entries are conceptually somewhat "semi-paraphrastic". 4 we acknowledge that schmitt and schütze (2021b) use hand-crafted prompts tuned for entailment detection, so they may be sub-optimal for separating same-label sub-groups; we argue that fixed-prompt lm tuning models are not too sensitive to their specific prompts @xcite ; nonetheless, we also report results from a continuous-prompt model @xcite in appendix b, where results are very similar. if a method is sensitive to directional entailments, it should be able to discriminate between each pair of the four sub-groups. particularly, it should additionally be able to separate sub-groups in the upper triangle of the mesh, coloured red in figure 1 . among these three tests of directionality, dirtrue-dirfalse is the most challenging: a method with no sensitivity to directionality should be at chance and get 0% auc norm ; this is traditionally called the directional subset @xcite . for the other two subsets, a symmetric measure would do above random by identifying entries in dirtrue / dirfalse as statistically less similar than paraphrases. below we discuss findings around the mesh and the triangle. the s&s model yields 77.7% auc norm when trained and tested on the full levy-holt dataset, which we provide for readers' reference. ## boolean open qa (booqa) dataset we have shown that the levyholt directional subset is especially infested with dataset artefacts and is not a robust benchmark for supervised methods. with the directionality triangle, we have shown evidence that lms have limited ability to learn directional entailments. however, this experiment is tedious and inconclusive: even solving the triangle does not guarantee solving directionality, as artefacts always persist; besides, we are still unable to compare between supervised and unsupervised methods. to establish an extrinsic, robust and realistic evaluation benchmark for directional predicate entailments, and to encourage future research on the topic, we present the boolean open-qa dataset (booqa). booqa is an automatically constructed dataset in english and chinese. it draws inspirations from the machine reading at scale task @xcite as well as the qa evaluation in @xcite . the task is formalized as: given a proposition and a large pool of context articles, determine whether the queried proposition is true based on the context pool. foot_4 ## related work related work to our paper broadly fall into three categories: language-model prompting, entailment graphs and natural language inference. lm-prompting methods convert input in target tasks into natural language sentences (prompts), thereby converting target tasks into either language modelling or sentence classification. @xcite probed lm for factual knowledge, where @xcite proposed automatic prompt discovery; on the other hand, gpt-3 @xcite showed large lms can do few-shot / zeroshot transfer with prompts, and schick and schütze (2021) showed a similar effect with smaller lms and natural language prompts. @xcite provided a comprehensive review of lm-prompting, which we refer readers to for more details. entailment graphs (egs) are graphs of predicates where directed edges represent entailment relations between pairs of predicates. weights on edges are entailment scores, induced from large textual corpora via unsupervised learning, based on directional co-occurrence signals; examples include binc @xcite , link prediction @xcite and the lm-driven cnce @xcite . being fully unsupervised, egs are sheltered from artefacts in training data, but are at the same time subject to noisy co-occurrence signals and sparsity common to discrete language resources. notably, while cnce eg (2021) uses lm, it involves contextualized encodings for premises, and uncontextualized encodings for hypotheses, trained from scratch with millions of unsupervised data entries. the architecture and abundant supervision enables cnce to encode directional entailments. egt2 @xcite is another lm-driven eg contemporary to our paper, with intermediate finetuning on mnli @xcite and ppdb @xcite annotations. they report good resuts on the directional subset of levyholt 13 , but perform mediocrely on booqa. we speculate that this could be due to instabilities of the small levyholt directional test set. natural language inference is concerned with the general task of "does sentence x entail sentence y?". classic nli datasets such as snli @xcite or mnli @xcite are populated with entries involving hypernyms of nouns, logic reasoning like a ∧ @xmath2, whereas predicate entailments are rarely involved. predicate entailment datasets like levyholt @xcite , sherliic @xcite or our booqa dataset, are a subclass of nli datasets focused on inference with predicates, where the arguments and adjuncts are carried over from premise to hypothesis. in particular, levyholt and our booqa dataset both feature the property of directionality, where the entailment is valid in one direction, but invalid in the other. general nli datasets are prune to simple dataset artefacts such as length, lexical overlaps or hypothesis-only models @xcite @xcite . these echo our observations on predicate entailments, and underscore the importance of a robust and extrinsic evaluation. 13 their reported directional subset is different from the original one in @xcite , see appendix h for details. ## conclusion in this paper, we show that existing lm prompting methods show limited ability to learn directional entailments, and instead overfit to dataset artefacts in the levyholt directional subset. we present booqa, a robust and extrinsic multilingual evaluation benchmark on directional predicate entailments as a boolean open qa dataset. on booqa, we show that lm prompting methods do not learn to pay attention to directionality from directional supervision, while the entailment graph methods remain limited by sparsity. we point to the generalisation of entailment graphs to continuous spaces and directionality-oriented intermediate pre-training of lms as directions of future work.