Search Results for author:
Found 16 papers, 7 papers with code
Language Model Pretraining and Transfer Learning for Very Low Resource Languages
This paper describes our submission for the shared task on Unsupervised MT and Very Low Resource Supervised MT at WMT 2021.
Granite Embedding Models
We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, with both English and Multilingual capabilities.
MILU: A Multi-task Indic Language Understanding Benchmark
Models also perform better in high resource languages as compared to low resource ones.
Evaluating the Instruction-following Abilities of Language Models using Knowledge Tasks
In this work, we focus our attention on developing a benchmark for instruction-following where it is easy to verify both task performance as well as instruction-following capabilities.
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi.
Mistral-SPLADE: LLMs for better Learned Sparse Retrieval
We use Mistral as the backbone to develop our Learned Sparse Retriever similar to SPLADE and train it on a subset of sentence-transformer data which is often used for training text embedding models.
Hindi-BEIR : A Large Scale Retrieval Benchmark in Hindi
Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi.
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages
Large Language Models (LLMs) have demonstrated remarkable zero-shot and few-shot capabilities in unseen tasks, including context-grounded question answering (QA) in English.
Airavata: Introducing Hindi Instruction-tuned LLM
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi.
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities
To demonstrate this fact, we release a Pragmatics Understanding Benchmark (PUB) dataset consisting of fourteen tasks in four pragmatics phenomena, namely, Implicature, Presupposition, Reference, and Deixis.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #53 on
Code Generation
on MBPP
HiNER: A Large Hindi Named Entity Recognition Dataset
We use different language models to perform the sequence labelling task for NER and show the efficacy of our data by performing a comparative evaluation with models trained on another dataset available for the Hindi NER task.
Ranked #1 on
Named Entity Recognition (NER)
on HiNER-original
Cognitively Aided Zero-Shot Automatic Essay Grading
Automatic essay grading (AEG) is a process in which machines assign a grade to an essay written in response to a topic, called the prompt.
Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing
Our approach uses templates to prepare the input sequence for the language model.
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
To demonstrate the efficacy of this multi-task learning based approach to automatic essay grading, we collect gaze behaviour for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the essays, numbering over 7000 essays.
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER
Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages.
