Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary (2023)

Abstract: The non-humanlike behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness. A striking phenomenon of such faulty behaviours is the generation of inconsistent predictions, which produces logically contradictory results, such as generating different predictions for texts delivering the same meaning or violating logical properties. Previous studies exploited data augmentation or implemented specialised loss functions to alleviate the issue. However, their usage is limited, because they consume expensive training resources for large-sized PLMs and can only handle a certain consistency type. To this end, we propose a practical approach that alleviates the inconsistent behaviour issue by fundamentally improving PLMs’ meaning awareness. Based on the conceptual role theory, our method allows PLMs to capture accurate meaning by learning precise interrelationships between concepts from word-definition pairs in a dictionary. Next, we propose an efficient parameter integration technique that updates only a few additional parameters to combine the learned interrelationship with PLMs’ pre-trained knowledge. Our experimental results reveal that the approach can concurrently improve multiple types of consistency, enables efficient knowledge integration, and easily applies to other languages.

## introduction ai systems that behave like humans can be more reliable and trustworthy @xcite . however, despite striking advances in various language tasks that modern plms have made, recent evidence of their non-humanlike behaviours @xcite @xcite @xcite ) casts doubts on their trustworthiness, suggesting that plms' language understanding ability is far below that of humans. the suggestive evidence revealing plms' incapability of meaning-understanding is inconsistent behaviours @xcite . unlike humans, they exhibit logically contradictory behaviours in many respects @xcite , such as generating different predictions for semantically identical texts @xcite or violating logical properties, e.g., the logical negation property @xcite @xcite , symmetry @xcite @xcite , or transitivity @xcite . such inconsistent behaviours undermine models' trustworthiness and limit their practical usefulness, particularly in risk-sensitive fields where even a minor undesirable action could lead to catastrophic consequences. previous works tried to alleviate the inconsistency issues through data augmentation @xcite or introducing specialised loss functions @xcite . these approaches, however, have limitations, as they can only address a specific type of consistency and are unable to handle others. for instance, consistency regularisation loss, which enforces a model's predictive distribution on the original and its paraphrased inputs to be similar @xcite , cannot resolve the violation of the logical negation property or symmetry. moreover, these approaches require expensive resources, which is practically inefficient, i.e., using a lot of computing resources for training large plms is impractical, and collecting auxiliary data (e.g., paraphrased texts) is demanding for low-resource languages. to this end, we propose a comprehensive solution that can concurrently enhance multiple types of consistency in a resource-efficient manner. based on previous studies that demonstrated the limitation of the distributional hypothesis (a fundamental theory underlying modern plms) in learning the meaning of natural language @xcite , we hypothesise that a leading cause contributing to the inconsistent behaviour is the incapability of plms to capture the precise meaning of language. thus, our proposed approach is centred around mitigating plms' deficiencies by leveraging an auxiliary model that is trained to imitate human cognition, thereby enhancing the awareness of semantic meaning. our underpinning assumption is the conceptual role theory, a convincing theory that accounts for human cognition. based on the theory, by which the interrelationship between concepts predominantly determines a word's meaning @xcite @xcite , our approach first learns abundant symbol meanings by tracking more precise interconnections through worddefinition pairs in a dictionary. next, we propose a training-efficient parameter integration method that combines the learned parameters with those of existing plms. this enables the fine-tuning process to update only a small number of parameters. our contributions can be briefly summarised as follows: ## related work analysis on consistency. most widely conducted studies regarding consistency are based on semantic equivalence, i.e., a model should generate the same predictions for text inputs conveying identical meanings. several works observed that when plms perform masked language modelling, they generate different predictions for queries where an object is replaced with its plural form @xcite or for paraphrased queries @xcite . @xcite introduced a consistency regularisation method to alleviate the inconsistent issue that forces plm to produce similar predictive distributions for the original and paraphrased queries. @xcite proposed a novel consistency regularisation technique known as "prompt consistency", designed for zero-shot learning. this method encourages consistent predictions across various sets of prompts. another line of work investigated plms' violation of logical properties, such as symmetry @xcite @xcite and transitivity @xcite @xcite . recently, @xcite proposed the definition of language model's consistency based on behavioural consistency and classified previous works into three large categories: semantic, logical, and factual consistency. semantic consistency contains works based on semantic equivalence, which should always be satisfied regardless of the tasks and data. logical consistency only applies to tasks where a certain property holds and consists of four subcategories: negational, symmetric, transitivity, and additive consistency. unlike the abovementioned studies that concentrated on the prediction inconsistency of language models (lms), several research delved into the inconsistency of explanations generated by plms and large language models (llms) @xcite @xcite . ## summary and outlook trustworthiness is a highly recommended property that a good natural language processing (nlp) model should satisfy, especially in risk-sensitive fields, such as the legal or medical domain. however, recent studies have revealed that modern plms make so many mistakes that humans rarely commit and, hence, are not perfectly reliable. one such faulty phenomenon is plms' inconsistent behaviours. many studies proposed a remedy to resolve the issue by using data augmentation and designing loss functions that penalise inconsistent actions. however, such methods have disadvantages in that they can only deal with specific con-sistency types, hardly apply to low-resource languages, and require expensive training resources for large plms. in this paper, we proposed an approach that improves overall consistencies in a practical manner. we hypothesised that a compelling reason for inconsistent behaviours is the lack of meaningunderstanding ability and, hence, attempted to enhance plms' meaning awareness based on the conceptual role theory. we designed a learning task that leverages word-definition pairs in a dictionary to capture more precise interconnection between concepts and named the learned model crm. next, we employed a parameter integration technique that combines the weights of crm and plm to maximally exploit previously-and newly-learned knowledge. our findings suggest that improved meaning awareness largely contributes to enhancing multiple consistency types concurrently. in addition, we observed that further improvements are achievable through incorporating additional parameters possessing distinct inductive bias. finally, we verified that our approach can readily apply to languages beyond english. our experimental results suggest that investigating human cognition and transplanting it to learning objectives could be a key to trustworthy language ai.