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Found 11 papers, 5 papers with code
MMTEB: Massive Multilingual Text Embedding Benchmark
MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date.
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.
Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning
Neural Machine Translation models are extremely data and compute-hungry.
On the low-shot transferability of [V]-Mamba
The strength of modern large-scale neural networks lies in their ability to efficiently adapt to new tasks with few examples.
Airavata: Introducing Hindi Instruction-tuned LLM
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi.
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages that use non-Roman scripts.
An Empirical Study of In-context Learning in LLMs for Machine Translation
We first establish that ICL is primarily example-driven and not instruction-driven.
Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding
Cloud analysis is a critical component of weather and climate science, impacting various sectors like disaster management.
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages
Prior to this work, there was (i) no parallel training data spanning all 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India.
A Federated Approach for Hate Speech Detection
Hate speech detection has been the subject of high research attention, due to the scale of content created on social media.
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search
In this work, we aim to investigate how effectively we can leverage this exceptional learning ability to improve machine learning models.
