Singapore

EMNLP 2023

Quantifying the Dialect Gap in Large Language Models and its Causes Across Languages

performance correlation

dialect gap

automatic speech recognition

multilingual

machine translation

**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.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

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

workshop paper

How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench

The Conversational Machine Reading Comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic because of its wide applications. However, existing CMRC benchmarks in which each conversation is coupled with a static passage are inconsistent with real scenarios. In this regard, it is hard to evaluate model’s comprehension ability towards real scenarios. In this work, we propose the first Chinese CMRC benchmark Orca and further provide zero-
shot/few-shot settings to evaluate model’s generalization ability towards diverse domains. We collect 831 hot-topic driven conversations with 4,742 turns in total. Each turn of a conversation is assigned with a response-related passage, aiming to evaluate model’s comprehension ability more reasonably. The topics of conversations are collected from social media platform and cover 33 domains, trying to be consistent with real scenarios. Importantly, answers in Orca are all well-annotated natural responses rather than specific spans or short phrases in previous datasets. We implement two strong frameworks to tackle the challenge in Orca.
The results indicate there is substantial room for improvement for strong baselines such as ChatGPT on our CMRC benchmark.

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension

Prompt tuning is a parameter-efficient method, which learns soft prompts and conditions frozen language models to perform specific downstream tasks. Though effective, prompt tuning under few-shot settings on the one hand heavily relies on a good initialization of soft prompts. On the other hand, it can easily overfit to few-shot training samples, thereby undermining generalizability. Existing works leverage pre-training or supervised meta-learning to initialize soft prompts but they fail to data-efficiently generalize to unseen downstream tasks. To address the above problems, this paper proposes a novel Self-sUpervised meta-Prompt learning framework with MEta-gradient Regularization for few-shot generalization (SUPMER). SUPMER leverages self-supervised meta-learning with a diverse set of well-designed meta-training tasks to learn a universal prompt initialization for efficient adaptation using only unlabeled data. Additionally, it jointly meta-learns a gradient regularization function to transform raw gradients into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUPMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability.

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How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench

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Next from EMNLP 2023

How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench

Similar lecture

Quantifying the Dialect Gap and its Correlates Across Languages

Stay up to date with the latest Underline news!

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COMPANY

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