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20172026
most citedSparse Communication for Distributed Gradient Descent

552 citations · 816 across the 123 of their papers we have counts for

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Showing 2023 · cs.CLShow all

17 papers · 2 filters

cs.CL2023

COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances

Haryo Akbarianto Wibowo, Erland Hilman Fuadi, Made Nindyatama Nityasya +2

We present COPAL-ID, a novel, public Indonesian language common sense reasoning dataset. Unlike the previous Indonesian COPA dataset (XCOPA-ID), COPAL-ID incorporates Indonesian lo…

cs.CL2023

QASiNa: Religious Domain Question Answering using Sirah Nabawiyah

Muhammad Razif Rizqullah, Ayu Purwarianti, Alham Fikri Aji

Nowadays, Question Answering (QA) tasks receive significant research focus, particularly with the development of Large Language Model (LLM) such as Chat GPT [1]. LLM can be applied…

cs.CL2023

Low-Resource Clickbait Spoiling for Indonesian via Question Answering

Ni Putu Intan Maharani, Ayu Purwarianti, Alham Fikri Aji

Clickbait spoiling aims to generate a short text to satisfy the curiosity induced by a clickbait post. As it is a newly introduced task, the dataset is only available in English so…

cs.CL2023★ 23 cited

Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models

Neha Sengupta, Sunil Kumar Sahu, Bokang Jia +29

We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-…

cs.CL2023

NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages

Samuel Cahyawijaya, Holy Lovenia, Fajri Koto +15

Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused…

cs.CL2023★ 10 cited

Style Over Substance: Evaluation Biases for Large Language Models

Minghao Wu, Alham Fikri Aji

As large language models (LLMs) continue to advance, accurately and comprehensively evaluating their performance becomes increasingly challenging. Ranking the relative performance…

cs.CL2023

WebIE: Faithful and Robust Information Extraction on the Web

Chenxi Whitehouse, Clara Vania, Alham Fikri Aji +2

Extracting structured and grounded fact triples from raw text is a fundamental task in Information Extraction (IE). Existing IE datasets are typically collected from Wikipedia arti…

cs.CL2023★ 12 cited

Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

Jinheon Baek, Alham Fikri Aji, Amir Saffari

Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training. H…

cs.CL2023

On "Scientific Debt" in NLP: A Case for More Rigour in Language Model Pre-Training Research

Made Nindyatama Nityasya, Haryo Akbarianto Wibowo, Alham Fikri Aji +4

This evidence-based position paper critiques current research practices within the language model pre-training literature. Despite rapid recent progress afforded by increasingly be…

cs.CL2023★ 1 cited

Multi-lingual and Multi-cultural Figurative Language Understanding

Anubha Kabra, Emmy Liu, Simran Khanuja +6

Figurative language permeates human communication, but at the same time is relatively understudied in NLP. Datasets have been created in English to accelerate progress towards meas…

cs.CL2023★ 2 cited

GlobalBench: A Benchmark for Global Progress in Natural Language Processing

Yueqi Song, Catherine Cui, Simran Khanuja +9

Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-opt…

cs.CL2023★ 6 cited

Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation

Haonan Li, Fajri Koto, Minghao Wu +2

Instruction tuning has shown great promise in improving the performance of large language models. However, research on multilingual instruction tuning has been limited due to the s…

cs.CL2023★ 21 cited

M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection

Yuxia Wang, Jonibek Mansurov, Petar Ivanov +12

Large language models (LLMs) have demonstrated remarkable capability to generate fluent responses to a wide variety of user queries. However, this has also raised concerns about th…

cs.CL2023★ 3 cited

LLM-powered Data Augmentation for Enhanced Cross-lingual Performance

Chenxi Whitehouse, Monojit Choudhury, Alham Fikri Aji

This paper explores the potential of leveraging Large Language Models (LLMs) for data augmentation in multilingual commonsense reasoning datasets where the available training data…

cs.CL2023★ 2 cited

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

Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz +2

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 pro…

cs.CL2023★ 25 cited

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

Minghao Wu, Abdul Waheed, Chiyu Zhang +2

Large language models (LLMs) with instruction fine-tuning demonstrate superior generative capabilities. However, these models are resource-intensive. To alleviate this issue, we ex…

cs.CL2023★ 6 cited

Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages

Zheng-Xin Yong, Ruochen Zhang, Jessica Zosa Forde +12

While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processi…