Survey on Computational Approaches to Implicature (2024)

Abstract: This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models’ capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs’ understanding of implicature and facilitate better human-computer interaction.

## introduction humans have long been fascinated by how language conveys meaning through words, intentions, and implications, prompting philosophers and linguists to explore its role in communicating information and performing actions. @xcite introduced the concept of speech acts, stating that utterances can perform actions beyond merely stating facts. he distinguished between locutionary acts, illocutionary acts, and perlocutionary acts. a locutionary act is the act of making an utterance with a specific sense and reference, an illocutionary act is the intentional action performed by the speaker through the utterance, and a perlocutionary act is the effect the utterance has on the listener. @xcite expanded speech act theory by categorizing illocutionary acts into five types: assertives (statements of fact), directives (requests or * * equal contribution. commands), commissives (promises), expressives (emotional expressions), and declarations (statements that change reality, such as making a decision official). he also introduced indirect speech acts, where the intended meaning differs from the literal words. this is crucial to implicature, as it highlights how speakers can imply actions or intentions indirectly, such as saying, "it's getting late," to suggest it's time to leave without explicitly stating it. implicature introduced by @xcite , refers to the implied meanings conveyed by speakers that extend beyond literal interpretations. it relies on the cooperative principle and conversational maxims, where violations lead to additional meanings, i.e., implicatures or pragmatic effects. ## implicature in computational linguistics understanding implicature is essential in nlp tasks like dialogue systems. despite the success of llms, their ability to handle implicature remains largely unexplored. this survey reviews current approaches, datasets, and future directions in the field. ## advances in computational implicature recent advances in llms have enabled the tackling of complex nlp tasks involving reasoning and inference, yet implicature continues to pose a significant challenge even for the most advanced models, as highlighted by the following key llms and their performance on implicature tasks (scores in percentages): while gpt-4 outperforms earlier models, it still falls short of human-level understanding, indicating that scaling alone is insufficient for resolving complex implicatures. notably, using accuracy as a metric is further justified by @xcite who demonstrated the effectiveness of binary classification for evaluating implicature tasks. ## future scope of implicature to improve the understanding of implicature, future models should focus on tasks that involve pragmatic reasoning, integrating world knowledge and social norms. this approach will enable more effective implied communication and enhance humancomputer interaction. future developments could involve expanding current models, which often have limited capacities, through crowd-sourcing efforts and by introducing upgraded versions of existing datasets, as demonstrated with circaplus and the circa dataset. additionally, utilizing established benchmarks such as hellaswag, arc, and mmlu for data augmentation could further enhance these models. broadening the scope of current implicature datasets to cover a wider range of conversational contexts and diversifying the types of implicature represented will create more realistic benchmarks for model evaluation. it is essential to incorporate data from real-life conversations, particularly in naturalistic settings, to improve model gener-alization and ensure that nlp systems can effectively navigate the complexities of human language across various scenarios. ## conclusion in conclusion, the study of implicature remains a critical area within computational linguistics, presenting unique challenges that current models struggle to address effectively. despite advancements in large language models (llms), their limited ability to grasp the nuances of implicature underscores the need for more comprehensive datasets and refined training methodologies. by integrating insights from grice's theory and focusing on pragmatic reasoning tasks, future research can help bridge the gap between human and machine understanding of indirect communication. enhancing models with diverse conversational contexts will improve their performance in implicature tasks and enhance the overall effectiveness of nlp systems in real-world applications.