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Found 16 papers, 8 papers with code
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.
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning
Reasoning is a fundamental capability of large language models (LLMs), enabling them to comprehend, analyze, and solve complex problems.
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
Monocular 3D object detection is a challenging task in autonomous systems due to the lack of explicit depth information in single-view images.
OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations
With the rise of deep learning, facial recognition technology has seen extensive research and rapid development.
Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense
In this paper, we demonstrate using false friends -- words that are orthographically similar but have completely different meanings in two languages -- as a possible approach to pinpoint the limitation of cross-lingual sense disambiguation in LLMs.
The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling
In particular, we ask (1) when two languages employ the same morphosyntactic processes, do LLMs handle them using shared internal circuitry?
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.
MINERS: Multilingual Language Models as Semantic Retrievers
Words have been represented in a high-dimensional vector space that encodes their semantic similarities, enabling downstream applications such as retrieving synonyms, antonyms, and relevant contexts.
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.
Multilingual Large Language Models Are Not (Yet) Code-Switchers
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.
Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages
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 processing (NLP) research.
CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization
Cross-lingual summarization (CLS) has attracted increasing interest in recent years due to the availability of large-scale web-mined datasets and the advancements of multilingual language models.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain
In the pursuit of natural language understanding, there has been a long standing interest in tracking state changes throughout narratives.
LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis
Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks.
Brown University at TREC Deep Learning 2019
This paper describes Brown University's submission to the TREC 2019 Deep Learning track.
