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Found 11 papers, 2 papers with code
XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference
In this paper, we use machine translation methods to construct a multilingual tabular NLI dataset, namely XINFOTABS, which expands the English tabular NLI dataset of INFOTABS to ten diverse languages.
Fine-tuning Distributional Semantic Models for Closely-Related Languages
In this paper we compare the performance of three models: SGNS (skip-gram negative sampling) and augmented versions of SVD (singular value decomposition) and PPMI (Positive Pointwise Mutual Information) on a word similarity task.
Exploring Pretraining via Active Forgetting for Improving Cross Lingual Transfer for Decoder Language Models
Prior works have shown that encoder-only models such as BERT or XLM-RoBERTa show impressive cross lingual transfer of their capabilities from English to other languages.
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages in which the model is already proficient (usually English).
Improving Self Consistency in LLMs through Probabilistic Tokenization
Prior research has demonstrated noticeable performance gains through the use of probabilistic tokenizations, an approach that involves employing multiple tokenizations of the same input string during the training phase of a language model.
Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts
Additionally, the approach should generalize inductively to novel structures of different sizes or complex structures expressed as a hierarchical composition of previously learned concepts.
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models
Prior work on multilingual evaluation has shown that there is a large gap between the performance of LLMs on English and other languages.
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
We also perform a study on data contamination and find that several models are likely to be contaminated with multilingual evaluation benchmarks, necessitating approaches to detect and handle contamination while assessing the multilingual performance of LLMs.
Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing.
A Review of Deep Learning Techniques for Protein Function Prediction
Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification.
IndicXNLI: Evaluating Multilingual Inference for Indian Languages
While Indic NLP has made rapid advances recently in terms of the availability of corpora and pre-trained models, benchmark datasets on standard NLU tasks are limited.
