2,451 research outputs found
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
Multimodal Hate Speech Detection from Bengali Memes and Texts
Numerous works have been proposed to employ machine learning (ML) and deep
learning (DL) techniques to utilize textual data from social media for
anti-social behavior analysis such as cyberbullying, fake news propagation, and
hate speech mainly for highly resourced languages like English. However,
despite having a lot of diversity and millions of native speakers, some
languages such as Bengali are under-resourced, which is due to a lack of
computational resources for natural language processing (NLP). Like English,
Bengali social media content also includes images along with texts (e.g.,
multimodal contents are posted by embedding short texts into images on
Facebook), only the textual data is not enough to judge them (e.g., to
determine they are hate speech). In those cases, images might give extra
context to properly judge. This paper is about hate speech detection from
multimodal Bengali memes and texts. We prepared the only multimodal hate speech
detection dataset1 for a kind of problem for Bengali. We train several neural
architectures (i.e., neural networks like Bi-LSTM/Conv-LSTM with word
embeddings, EfficientNet + transformer architectures such as monolingual Bangla
BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) jointly analyze textual
and visual information for hate speech detection. The Conv-LSTM and XLM-RoBERTa
models performed best for texts, yielding F1 scores of 0.78 and 0.82,
respectively. As of memes, ResNet152 and DenseNet201 models yield F1 scores of
0.78 and 0.7, respectively. The multimodal fusion of mBERT-uncased +
EfficientNet-B1 performed the best, yielding an F1 score of 0.80. Our study
suggests that memes are moderately useful for hate speech detection in Bengali,
but none of the multimodal models outperform unimodal models analyzing only
textual data
MSIR@FIRE: A Comprehensive Report from 2013 to 2016
[EN] India is a nation of geographical and cultural diversity where over 1600 dialects are spoken by the people. With the technological advancement, penetration of the internet and cheaper access to mobile data, India has recently seen a sudden growth
of internet users. These Indian internet users generate contents either in English or in other vernacular Indian languages.
To develop technological solutions for the contents generated by the Indian users using the Indian languages, the Forum
for Information Retrieval Evaluation (FIRE) was established and held for the first time in 2008. Although Indian languages
are written using indigenous scripts, often websites and user-generated content (such as tweets and blogs) in these Indian
languages are written using Roman script due to various socio-cultural and technological reasons. A challenge that search
engines face while processing transliterated queries and documents is that of extensive spelling variation. MSIR track was
first introduced in 2013 at FIRE and the aim of MSIR was to systematically formalize several research problems that one must
solve to tackle the code mixing in Web search for users of many languages around the world, develop related data sets, test
benches and most importantly, build a research community focusing on this important problem that has received very little attention. This document is a comprehensive report on the 4 years of MSIR track evaluated at FIRE between 2013 and 2016.Somnath Banerjee and Sudip Kumar Naskar are supported by Media Lab Asia, MeitY, Government of India, under the Visvesvaraya PhD Scheme for Electronics & IT. The work of Paolo Rosso was partially supported by the MISMIS research project PGC2018-096212-B-C31 funded by the Spanish MICINN.Banerjee, S.; Choudhury, M.; Chakma, K.; Kumar Naskar, S.; Das, A.; Bandyopadhyay, S.; Rosso, P. (2020). MSIR@FIRE: A Comprehensive Report from 2013 to 2016. SN Computer Science. 1(55):1-15. https://doi.org/10.1007/s42979-019-0058-0S115155Ahmed UZ, Bali K, Choudhury M, Sowmya VB. Challenges in designing input method editors for Indian languages: the role of word-origin and context. 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In: Proceedings of the workshop on modeling, learning and mining for cross/multilinguality (MultiLingMine 2016), co-located with the 38th European Conference on Information Retrieval (ECIR). 2016.Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. Named entity recognition on code-mixed cross-script social media content. Comput Sistemas. 2017;21(4):681–92.Barman U, Das A, Wagner J, Foster J. Code mixing: a challenge for language identification in the language of social media. In: Proceedings of the first workshop on computational approaches to code switching. 2014. pp. 13–23.Bhardwaj P, Pakray P, Bajpeyee V, Taneja A. Information retrieval on code-mixed Hindi–English tweets. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Bhargava R, Khandelwal S, Bhatia A, Sharmai Y. Modeling classifier for code mixed cross script questions. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Bhattacharjee D, Bhattacharya, P. Ensemble classifier based approach for code-mixed cross-script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Chakma K, Das A. CMIR: a corpus for evaluation of code mixed information retrieval of Hindi–English tweets. In: The 17th international conference on intelligent text processing and computational linguistics (CICLING). 2016.Choudhury M, Chittaranjan G, Gupta P, Das A. Overview of fire 2014 track on transliterated search. Proceedings of FIRE. 2014. pp. 68–89.Ganguly D, Pal S, Jones GJ. Dcu@fire-2014: fuzzy queries with rule-based normalization for mixed script information retrieval. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 80–85.Gella S, Sharma J, Bali K. Query word labeling and back transliteration for Indian languages: shared task system description. FIRE Working Notes. 2013;3.Gupta DK, Kumar S, Ekbal A. Machine learning approach for language identification and transliteration. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 60–64.Gupta P, Bali K, Banchs RE, Choudhury M, Rosso P. Query expansion for mixed-script information retrieval. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, ACM, 2014. pp. 677–686.Gupta P, Rosso P, Banchs RE. Encoding transliteration variation through dimensionality reduction: fire shared task on transliterated search. In: Fifth forum for information retrieval evaluation. 2013.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for information retrieval. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for text classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst. 2002;20:422–46. https://doi.org/10.1145/582415.582418.Joshi H, Bhatt A, Patel H. Transliterated search using syllabification approach. In: Forum for information retrieval evaluation. 2013.King B, Abney S. Labeling the languages of words in mixed-language documents using weakly supervised methods. In: Proceedings of NAACL-HLT, 2013. pp. 1110–1119.Londhe N, Srihari RK. Exploiting named entity mentions towards code mixed IR: working notes for the UB system submission for MSIR@FIRE’16. 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Is Attention always needed? A Case Study on Language Identification from Speech
Language Identification (LID) is a crucial preliminary process in the field
of Automatic Speech Recognition (ASR) that involves the identification of a
spoken language from audio samples. Contemporary systems that can process
speech in multiple languages require users to expressly designate one or more
languages prior to utilization. The LID task assumes a significant role in
scenarios where ASR systems are unable to comprehend the spoken language in
multilingual settings, leading to unsuccessful speech recognition outcomes. The
present study introduces convolutional recurrent neural network (CRNN) based
LID, designed to operate on the Mel-frequency Cepstral Coefficient (MFCC)
characteristics of audio samples. Furthermore, we replicate certain
state-of-the-art methodologies, specifically the Convolutional Neural Network
(CNN) and Attention-based Convolutional Recurrent Neural Network (CRNN with
attention), and conduct a comparative analysis with our CRNN-based approach. We
conducted comprehensive evaluations on thirteen distinct Indian languages and
our model resulted in over 98\% classification accuracy. The LID model exhibits
high-performance levels ranging from 97% to 100% for languages that are
linguistically similar. The proposed LID model exhibits a high degree of
extensibility to additional languages and demonstrates a strong resistance to
noise, achieving 91.2% accuracy in a noisy setting when applied to a European
Language (EU) dataset.Comment: Accepted for publication in Natural Language Engineerin
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
Aligned large language models (LLMs) demonstrate exceptional capabilities in
task-solving, following instructions, and ensuring safety. However, the
continual learning aspect of these aligned LLMs has been largely overlooked.
Existing continual learning benchmarks lack sufficient challenge for leading
aligned LLMs, owing to both their simplicity and the models' potential exposure
during instruction tuning. In this paper, we introduce TRACE, a novel benchmark
designed to evaluate continual learning in LLMs. TRACE consists of 8 distinct
datasets spanning challenging tasks including domain-specific tasks,
multilingual capabilities, code generation, and mathematical reasoning. All
datasets are standardized into a unified format, allowing for effortless
automatic evaluation of LLMs. Our experiments show that after training on
TRACE, aligned LLMs exhibit significant declines in both general ability and
instruction-following capabilities. For example, the accuracy of llama2-chat
13B on gsm8k dataset declined precipitously from 28.8\% to 2\% after training
on our datasets. This highlights the challenge of finding a suitable tradeoff
between achieving performance on specific tasks while preserving the original
prowess of LLMs. Empirical findings suggest that tasks inherently equipped with
reasoning paths contribute significantly to preserving certain capabilities of
LLMs against potential declines. Motivated by this, we introduce the
Reasoning-augmented Continual Learning (RCL) approach. RCL integrates
task-specific cues with meta-rationales, effectively reducing catastrophic
forgetting in LLMs while expediting convergence on novel tasks
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