Search Results for author: Md Tahmid Rahman Laskar

Found 27 papers, 10 papers with code

DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts

1 code implementation9 Aug 2024 Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty

Data-driven storytelling is a powerful method for conveying insights by combining narrative techniques with visualizations and text.

Story Generation

Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization

no code implementations29 Feb 2024 Md Tahmid Rahman Laskar, Elena Khasanova, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN

When using Large Language Models (LLMs) for this task, usually a new call to the LLM inference endpoint/API is triggered for each new query, even if the context stays the same.

Meeting Summarization

Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?

no code implementations1 Feb 2024 Xue-Yong Fu, Md Tahmid Rahman Laskar, Elena Khasanova, Cheng Chen, Shashi Bhushan TN

In this paper, we investigate whether smaller, compact LLMs are a good alternative to the comparatively Larger LLMs2 to address significant costs associated with utilizing LLMs in the real world.

Meeting Summarization

Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs

no code implementations1 Nov 2023 Xue-Yong Fu, Md Tahmid Rahman Laskar, Cheng Chen, Shashi Bhushan TN

In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models.

Benchmarking Question Answering +1

Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective

no code implementations30 Oct 2023 Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN

This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs).

Meeting Summarization

A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks

1 code implementation6 Oct 2023 Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, Jimmy Huang

While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.

ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries

1 code implementation26 Apr 2023 Raian Rahman, Rizvi Hasan, Abdullah Al Farhad, Md Tahmid Rahman Laskar, Md. Hamjajul Ashmafee, Abu Raihan Mostofa Kamal

Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user.

Data Summarization Hallucination

CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia

no code implementations31 Mar 2023 Md Tahmid Rahman Laskar, Mizanur Rahman, Israt Jahan, Enamul Hoque, Jimmy Huang

Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years.

Abstractive Text Summarization Text Generation

DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities

1 code implementation10 Oct 2022 Mohsinul Kabir, Tasnim Ahmed, Md. Bakhtiar Hasan, Md Tahmid Rahman Laskar, Tarun Kumar Joarder, Hasan Mahmud, Kamrul Hasan

Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data.

An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts

no code implementations COLING (WNUT) 2022 Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver

We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Improving Punctuation Restoration for Speech Transcripts via External Data

no code implementations WNUT (ACL) 2021 Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver

To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Extending Isolation Forest for Anomaly Detection in Big Data via K-Means

no code implementations27 Apr 2021 Md Tahmid Rahman Laskar, Jimmy Huang, Vladan Smetana, Chris Stewart, Kees Pouw, Aijun An, Stephen Chan, Lei Liu

Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup.

Anomaly Detection Intrusion Detection

Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection

1 code implementation14 Nov 2020 Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang

We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13. 1% in the QA datasets and 18. 7% in the CQA datasets compared to the previous state-of-the-art.

Answer Selection Community Question Answering +2

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

1 code implementation COLING 2020 Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang

In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query.

Abstractive Text Summarization Document Summarization +5

Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task

1 code implementation LREC 2020 Md Tahmid Rahman Laskar, Jimmy Xiangji Huang, Enamul Hoque

In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task.

Answer Selection Sentence +2

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