Understanding Language in the Era of Human-Machine Interaction: Challenges and Opportunities

Large language models (LLMs) have significantly advanced the development of interactional artificial intelligence (AI) systems. However, despite their remarkable achievements, LLMs still face challenges in truly comprehending human languages. Understanding language involves more than just parsing form and meaning; it requires access to context for a deeper comprehension. Equipping LLMs with linguistics-grounded capabilities remains a complex task, given the importance of discourse, pragmatics, and social context in language understanding.Understanding language is a doubly challenging task as it necessitates not only grasping the intrinsic capabilities of LLMs but also examining their impact and requirements in real-world applications. While LLMs have shown efficacy in various applications, the lack of supporting theories raises ethical concerns, particularly in applications involving human interaction.

The “Language Understanding in the Human-Machine Era” (LUHME) workshop aims to reinvigorate the discourse on the role of understanding in natural language use and its applications. It seeks to explore the necessity of language understanding in computational tasks like machine translation and natural language generation, as well as the contributions of language professionals in enhancing computational language understanding.

Topics of interest include: language understanding in LLMs, language grounding, psycholinguistic approaches to language understanding, discourse, pragmatics and language understanding, evaluation of language understanding, multi-modality and language understanding, socio-cultural aspects in understanding language, effects of language misunderstanding by computational models, manifestations of language understanding, distributional semantics and language understanding, linguistic theory and language understanding by machines, linguistic, world, and common sense knowledge in language understanding, machine translation and/or interpreting and language understanding, human vs. machine language understanding, role of language professionals in the LLMs era, and understanding language and explainable AI.