Multilingual Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks, exhibiting state-of-the-art performance through zero-shot or few-shot prompting methods. While there have been extensive studies on their abilities in monolingual tasks, the investigation of their potential in the context of code-switching (CSW), the practice of alternating languages within an utterance, remains relatively uncharted. In this paper, we provide a comprehensive empirical analysis of various multilingual LLMs, benchmarking their performance across four tasks: sentiment analysis, machine translation, summarization and word-level language identification. Our results indicate that despite multilingual LLMs exhibiting promising outcomes in certain tasks using zero or few-shot prompting, they still underperform in comparison to fine-tuned models of much smaller scales. We argue that current "multilingualism" in LLMs does not inherently imply proficiency with code-switching texts, calling for future research to bridge this discrepancy.

Multilingual Large Language Models Are Not (Yet) Code-Switchers

In this talk, we present SOCCER, the first information-sparse discourse state tracking dataset. It contains over 2,000 soccer matches with 135,000+ timestamped commentary, 31,000+ within-match events and other rich meta information. It expands the currently available state tracking collection that is used to be information-dense without exception, and provides the NLP community with diverse resources for distilling better state representation.

SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain

Multilinguality and Linguistic Diversity 2

technical paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

**Session Chair: ** Seokhwan Kim

12D-Oral: Language Resources and Evaluation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Ruochen Zhang

2

SHORT BIO

Presentations

Multilingual Large Language Models Are Not (Yet) Code-Switchers

SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain

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