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Found 41 papers, 20 papers with code
Can Pretrained Language Models Generate Persuasive, Faithful, and Informative Ad Text for Product Descriptions?
For any e-commerce service, persuasive, faithful, and informative product descriptions can attract shoppers and improve sales.
Handling Variance of Pretrained Language Models in Grading Evidence in the Medical Literature
In this paper, we investigate the utility of modern pretrained language models for the evidence grading system in the medical literature based on the ALTA 2021 shared task.
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization
Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document.
Easy-First Bottom-Up Discourse Parsing via Sequence Labelling
We propose a novel unconstrained bottom-up approach for rhetorical discourse parsing based on sequence labelling of adjacent pairs of discourse units (DUs), based on the framework of Koto et al. (2021).
Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian
Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian.
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation.
Llama-3.1-Sherkala-8B-Chat: An Open Large Language Model for Kazakh
Llama-3. 1-Sherkala-8B-Chat, or Sherkala-Chat (8B) for short, is a state-of-the-art instruction-tuned open generative large language model (LLM) designed for Kazakh.
Unveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in Procedural Text Comprehension
Despite the impressive performance of multilingual large language models (mLLMs) in various natural language processing tasks, their ability to understand procedural texts, particularly those with culture-specific content, remains largely unexplored.
Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts
Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users.
Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh
Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains.
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360.
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety.
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities.
Cracking the Code: Multi-domain LLM Evaluation on Real-World Professional Exams in Indonesia
While knowledge evaluation in large language models has predominantly focused on academic subjects like math and physics, these assessments often fail to capture the practical demands of real-world professions.
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1, 300 indigenous languages and a population of 671 million people.
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes.
IndoCulture: Exploring Geographically-Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces
Although commonsense reasoning is greatly shaped by cultural and geographical factors, previous studies have predominantly centered on cultures grounded in the English language, potentially resulting in an Anglocentric bias.
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models.
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages.
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU
In this work, we introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia.
NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages
We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets.
Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings
Large language models (LLMs) are highly adept at question answering and reasoning tasks, but when reasoning in a situational context, human expectations vary depending on the relevant cultural common ground.
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs.
CMMLU: Measuring massive multitask language understanding in Chinese
As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation
However, research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets across different languages.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages
At the center of the underlying issues that halt Indonesian natural language processing (NLP) research advancement, we find data scarcity.
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
In this work, we focus on developing resources for languages in Indonesia.
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia
NLP research is impeded by a lack of resources and awareness of the challenges presented by underrepresented languages and dialects.
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization
We present IndoBERTweet, the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually-trained Indonesian BERT model with additive domain-specific vocabulary.
Evaluating the Efficacy of Summarization Evaluation across Languages
We take a summarization corpus for eight different languages, and manually annotate generated summaries for focus (precision) and coverage (recall).
Discourse Probing of Pretrained Language Models
Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks.
Top-down Discourse Parsing via Sequence Labelling
We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020).
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FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences).
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP
Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research.
Liputan6: A Large-scale Indonesian Dataset for Text Summarization
In this paper, we introduce a large-scale Indonesian summarization dataset.
Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation
Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly attempted to define the morphology and syntax of Minangkabau, information processing in this language is still absent due to the scarcity of the annotated resource.
Improved Document Modelling with a Neural Discourse Parser
We empirically investigate the benefit of the proposed approach on two different tasks: abstractive summarization and popularity prediction of online petitions.
A Publicly Available Indonesian Corpora for Automatic Abstractive and Extractive Chat Summarization
In this paper we report our effort to construct the first ever Indonesian corpora for chat summarization.
