This study investigates the automation of metaanalysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies (support articles) to provide a comprehensive understanding. We know that a metaarticle provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor-intensive, time-consuming, and susceptible to human error, highlighting the need for automated pipelines to streamline the process. Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction. We automate and optimize the meta-analysis process by integrating Retrieval Augmented Generation (RAG). Tailored through prompt engineering and a new loss metric, Inverse Cosine Distance (ICD), designed for fine-tuning on large contextual datasets, LLMs efficiently generate structured meta-analysis content. Human evaluation then assesses relevance and provides information on model performance in key metrics. This research demonstrates that fine-tuned models outperform non-fine-tuned models, with fine-tuned LLMs generating 87.6% relevant meta-analysis abstracts. The relevance of the context, based on human evaluation, shows a reduction in irrelevancy from 4.56% to 1.9%. These experiments were conducted in a low-resource environment, highlighting the study’s contribution to enhancing the efficiency and reliability of meta-analysis automation.

This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and lexicon. Subtle changes in pronunciation and intonation are also influenced by geographic location, educational attainment, and socioeconomic status. Dialect standardization is needed to ensure effective communication, educational consistency, access to technology, economic opportunities, and the preservation of linguistic resources while respecting cultural diversity. Being the fifth most spoken language with around 55 distinct dialects spoken by 160 million people, addressing Bangla dialects is crucial for developing inclusive communication tools. However, limited research exists due to a lack of comprehensive datasets and the challenges of handling diverse dialects. With the advancement in multilingual Large Language Models (mLLMs), emerging possibilities have been created to address the challenges of dialectal Automated Speech Recognition (ASR) and Machine Translation (MT). This study presents an end-to-end pipeline for converting dialectal Noakhali speech to standard Bangla speech. This investigation includes constructing a large-scale diverse dataset with dialectal speech signals that tailored the fine-tuning process in ASR and LLM for transcribing the dialect speech to dialect text and translating the dialect text to standard Bangla text. Our experiments demonstrated that fine-tuning the Whisper ASR model achieved a CER of 0.8% and WER of 1.5%, while the BanglaT5 model attained a BLEU score of 41.6% for dialect-to-standard text translation. We completed our end-to-end pipeline for dialect standardization by utilizing AlignTTS, a text-to-speech (TTS) model. With potential applications across different dialects, this research lays the groundwork for future research into Bangla dialect standardization.

In this study, we present a Bangla multi-domain sentiment analysis dataset, named as SentiGOLD, developed using 70,000 samples, which was compiled from a variety of sources and annotated by a gender-balanced team of linguists. This dataset was created in accordance with a standard set of linguistic conventions that were established after multiple meetings between the Government of Bangladesh and a nationally recognized Bangla linguistics committee. Although there are standard sentiment analysis datasets available for English and other rich languages, there are not any such datasets in Bangla, especially because, there was no standard linguistics framework agreed upon by national stakeholders. Senti-GOLD derives its raw data from online video comments, social media posts and comments, blog posts and comments, news and numerous other sources. Throughout the development of this dataset, domain distribution and class distribution were rigorously maintained. SentiGOLD was created using data from a total of 30 domains (e.g. politics, entertainment, sports, etc.) and was labeled using 5 classes (e.g. strongly negative, weakly negative, neutral, weakly positive, and strongly positive). In order to maintain annotation quality, the national linguistics committee approved an annotation scheme to ensure a rigorous Inter Annotator Agreement (IAA) in a multi-annotator annotation scenario. This procedure yielded an IAA score of 0.88 using Fleiss' kappa method, which is elaborated upon in the paper. A protocol for intra- and cross-dataset evaluation was utilized in our efforts to develop a classification system as a standard. The cross-dataset evaluation was performed on the SentNoB dataset, which contains noisy Bangla text samples, thereby establishing a demanding test scenario. We also performed cross-dataset testing by employing zero-shot experiments, and our best model produced competitive performance, which exemplify our dataset's generalizability. Our top model attained a macro f1 of 0.62 (intra-dataset) for 5 classes establishing the benchmark for SentiGOLD, and 0.61 (cross-dataset from SentNoB) for 3 classes which stands comparable to the current state-of-the-art. Our fine-tuned sentiment analysis model\footnotehttps://sentiment.bangla.gov.bd can be accessed online.

The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPMs) and medical experts are at the core of responding to this continuously evolving pandemic situation and are working hard to contain the spread and severity of this relatively unknown virus. Biomedical researchers are continually discovering new information about this virus and communicating the findings through scientific articles. As such, it is crucial for HPMs and funding agencies to monitor the COVID-19 research trend globally on a regular basis. However, given the influx of biomedical research articles, monitoring COVID-19 research trends has become more challenging than ever, especially when HPMs want on-demand guided search techniques with a set of topics of interest in mind. Unfortunately, existing topic trend modeling techniques are unable to serve this purpose as (1) traditional topic models are unsupervised, and (2) HPMs in different regions may have different topics of interest that they want to track. To address this problem, we introduce a novel computational task in this article calledAd-Hoc Topic Tracking, which is essentially a combination ofzero-shottopic categorization and the spatio-temporal analysis task. We then propose multiplezero-shotclassification methods to solve this task by building on state-of-the-art language understanding techniques. Next, we picked the best-performing method based on its accuracy on a separate validation dataset and then applied it to a corpus of recent biomedical research articles to track COVID-19 research endeavors across the globe using a spatio-temporal analysis. A demo website has also been developed for HPMs to create custom spatio-temporal visualizations of COVID-19 research trends. The research outcomes demonstrate that the proposedzero-shotclassification methods can potentially facilitate further research on this important subject matter. At the same time, the spatio-temporal visualization tool will greatly assist HPMs and funding agencies in making well-informed policy decisions for advancing scientific research efforts.

Non-pharmaceutical Interventions (NPIs), such as Stay-at-Home, and Face-Mask-Mandate, are essential components of the public health response to contain an outbreak like COVID-19. However, it is very challenging to quantify the individual or joint effectiveness of NPIs and their impact on people from different racial and ethnic groups or communities in general. Therefore, in this paper, we study the following two research questions: 1) How can we quantitatively estimate the effectiveness of different NPI policies pertaining to the COVID-19 pandemic?; and 2) Do these policies have considerably different effects on communities from different races and ethnicity? To answer these questions, we model the impact of an NPI as a joint function of stringency and effectiveness over a duration of time. Consequently, we propose a novel stringency function that can provide an estimate of how strictly an NPI was implemented on a particular day. Next, we applied two popular tree-based discriminative classifiers, considering the change in daily COVID cases and death counts as binary target variables, while using stringency values of different policies as independent features. Finally, we interpreted the learned feature weights as the effectiveness of COVID-19 NPIs. Our experimental results suggest that, at the country level, restaurant closures and stay-at-home policies were most effective in restricting the COVID-19 confirmed cases and death cases respectively; and overall, restaurant closing was most effective in hold-down of COVID-19 cases at individual community levels such as Asian, White, Black, AIAN and, NHPI. Additionally, we also performed a comparative analysis between race-specific effectiveness and country-level effectiveness to see whether different communities were impacted differently. Our findings suggest that the different policies impacted communities (race and ethnicity) differently.