121 research outputs found
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP
Large Language Models (LLMs) have emerged as one of the most important
breakthroughs in natural language processing (NLP) for their impressive skills
in language generation and other language-specific tasks. Though LLMs have been
evaluated in various tasks, mostly in English, they have not yet undergone
thorough evaluation in under-resourced languages such as Bengali (Bangla). In
this paper, we evaluate the performance of LLMs for the low-resourced Bangla
language. We select various important and diverse Bangla NLP tasks, such as
abstractive summarization, question answering, paraphrasing, natural language
inference, text classification, and sentiment analysis for zero-shot evaluation
with ChatGPT, LLaMA-2, and Claude-2 and compare the performance with
state-of-the-art fine-tuned models. Our experimental results demonstrate an
inferior performance of LLMs for different Bangla NLP tasks, calling for
further effort to develop better understanding of LLMs in low-resource
languages like Bangla.Comment: First two authors contributed equall
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
This work presents BanglaNLG, a comprehensive benchmark for evaluating
natural language generation (NLG) models in Bangla, a widely spoken yet
low-resource language. We aggregate six challenging conditional text generation
tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue
generation in the process. Then, using a clean corpus of 27.5 GB of Bangla
data, we pretrain BanglaT5, a sequence-to-sequence Transformer model for
Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks,
outperforming several multilingual models by up to 9% absolute gain and 32%
relative gain. We are making the new dataset, the BanglaT5 language model, and
a leaderboard publicly available at https://github.com/csebuetnlp/BanglaNLG in
the hope of advancing future research and evaluation on Bangla NLG.Comment: Accepted at the Findings of EACL 202
Crosslingual Retrieval Augmented In-context Learning for Bangla
The promise of Large Language Models (LLMs) in Natural Language Processing
has often been overshadowed by their limited performance in low-resource
languages such as Bangla. To address this, our paper presents a pioneering
approach that utilizes cross-lingual retrieval augmented in-context learning.
By strategically sourcing semantically similar prompts from high-resource
language, we enable multilingual pretrained language models (MPLMs), especially
the generative model BLOOMZ, to successfully boost performance on Bangla tasks.
Our extensive evaluation highlights that the cross-lingual retrieval augmented
prompts bring steady improvements to MPLMs over the zero-shot performance.Comment: In The 1st Bangla Language Processing (BLP) Workshop, held in
conjunction with The Conference on Empirical Methods in Natural Language
Processing (EMNLP), December 202
Comparative study on Judgment Text Classification for Transformer Based Models
This work involves the usage of various NLP models to predict the winner of a
particular judgment by the means of text extraction and summarization from a
judgment document. These documents are useful when it comes to legal
proceedings. One such advantage is that these can be used for citations and
precedence reference in Lawsuits and cases which makes a strong argument for
their case by the ones using it. When it comes to precedence, it is necessary
to refer to an ample number of documents in order to collect legal points with
respect to the case. However, reviewing these documents takes a long time to
analyze due to the complex word structure and the size of the document. This
work involves the comparative study of 6 different self-attention-based
transformer models and how they perform when they are being tweaked in 4
different activation functions. These models which are trained with 200
judgement contexts and their results are being judged based on different
benchmark parameters. These models finally have a confidence level up to 99%
while predicting the judgment. This can be used to get a particular judgment
document without spending too much time searching relevant cases and reading
them completely.Comment: 28 pages with 9 figure
LR-Sum: Summarization for Less-Resourced Languages
This preprint describes work in progress on LR-Sum, a new
permissively-licensed dataset created with the goal of enabling further
research in automatic summarization for less-resourced languages. LR-Sum
contains human-written summaries for 40 languages, many of which are
less-resourced. We describe our process for extracting and filtering the
dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022). The
source data is public domain newswire collected from from Voice of America
websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0),
making it one of the most openly-licensed multilingual summarization datasets.
We describe how we plan to use the data for modeling experiments and discuss
limitations of the dataset
Document Similarity of Czech Supreme Court Decisions
Retrieval of court decisions dealing with a similar legal matter is a prevalent task performed by lawyers as it is a part of a relevant decision-making practice review. In spite of the natural language processing methods that are currently available, this legal research is still mostly done through Boolean searches or by contextual retrieval. In this study, it is experimentally verified whether the doc2vec method together with cosine similarity, can automatically retrieve the Czech Supreme Court decisions dealing with a similar legal issue as a given decision. Furthermore, the limits and challenges of these methods and its application on the Czech Supreme Court decisions are discussed
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study
In an era where the silent struggle of underdiagnosed depression pervades
globally, our research delves into the crucial link between mental health and
social media. This work focuses on early detection of depression, particularly
in extroverted social media users, using LLMs such as GPT 3.5, GPT 4 and our
proposed GPT 3.5 fine-tuned model DepGPT, as well as advanced Deep learning
models(LSTM, Bi-LSTM, GRU, BiGRU) and Transformer models(BERT, BanglaBERT,
SahajBERT, BanglaBERT-Base). The study categorized Reddit and X datasets into
"Depressive" and "Non-Depressive" segments, translated into Bengali by native
speakers with expertise in mental health, resulting in the creation of the
Bengali Social Media Depressive Dataset (BSMDD). Our work provides full
architecture details for each model and a methodical way to assess their
performance in Bengali depressive text categorization using zero-shot and
few-shot learning techniques. Our work demonstrates the superiority of
SahajBERT and Bi-LSTM with FastText embeddings in their respective domains also
tackles explainability issues with transformer models and emphasizes the
effectiveness of LLMs, especially DepGPT, demonstrating flexibility and
competence in a range of learning contexts. According to the experiment
results, the proposed model, DepGPT, outperformed not only Alpaca Lora 7B in
zero-shot and few-shot scenarios but also every other model, achieving a
near-perfect accuracy of 0.9796 and an F1-score of 0.9804, high recall, and
exceptional precision. Although competitive, GPT-3.5 Turbo and Alpaca Lora 7B
show relatively poorer effectiveness in zero-shot and few-shot situations. The
work emphasizes the effectiveness and flexibility of LLMs in a variety of
linguistic circumstances, providing insightful information about the complex
field of depression detection models
Language Identification: Contrivance Learning Process Using Web Based Disquisition
Language identification is the foremost task in the study of linguistics .The projections of language identification & conversions such as Google translate or any other hypothetical translator works in wonders. The mechanism of detecting the language performed by these translators is a real marvel. Hence in this divertissement it is of the primary importance to study the methods of identifying the language. In this paper, the methodologies of recognizing some of the Natural Languages such as English, Kannada, Hindi & Telugu is explained on the basis of N-Gram algorithm and the respective vowels and consonants of each of the languages are retrieved and stored for building the syntactic structure of the corpus
FaceAtt: Enhancing Image Captioning with Facial Attributes for Portrait Images
Automated image caption generation is a critical area of research that
enhances accessibility and understanding of visual content for diverse
audiences. In this study, we propose the FaceAtt model, a novel approach to
attribute-focused image captioning that emphasizes the accurate depiction of
facial attributes within images. FaceAtt automatically detects and describes a
wide range of attributes, including emotions, expressions, pointed noses, fair
skin tones, hair textures, attractiveness, and approximate age ranges.
Leveraging deep learning techniques, we explore the impact of different image
feature extraction methods on caption quality and evaluate our model's
performance using metrics such as BLEU and METEOR. Our FaceAtt model leverages
annotated attributes of portraits as supplementary prior knowledge for our
portrait images before captioning. This innovative addition yields a subtle yet
discernible enhancement in the resulting scores, exemplifying the potency of
incorporating additional attribute vectors during training. Furthermore, our
research contributes to the broader discourse on ethical considerations in
automated captioning. This study sets the stage for future research in refining
attribute-focused captioning techniques, with a focus on enhancing linguistic
coherence, addressing biases, and accommodating diverse user needs
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