1 code implementation • CL (ACL) 2022 • Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang
In this article, we address this challenge by exploring a series of domain adaptation techniques.
1 code implementation • 9 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.
no code implementations • 4 Jul 2024 • Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation.
no code implementations • 1 Jun 2024 • Mohammed Saidul Islam, Raian Rahman, Ahmed Masry, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, Enamul Hoque
To bridge the gap, this paper presents the first comprehensive evaluation of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks.
no code implementations • 29 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.
no code implementations • 18 Feb 2024 • Jiajia Wang, Jimmy X. Huang, Xinhui Tu, Junmei Wang, Angela J. Huang, Md Tahmid Rahman Laskar, Amran Bhuiyan
Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems.
no code implementations • 1 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.
no code implementations • 1 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.
no code implementations • 30 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).
1 code implementation • 6 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.
no code implementations • 22 Sep 2023 • Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
Large Language Models (LLMs) have emerged as one of the most important breakthroughs in NLP for their impressive skills in language generation and other language-specific tasks.
Abstractive Text Summarization Natural Language Inference +6
no code implementations • 7 Jun 2023 • Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, Jimmy Huang
ChatGPT is a large language model developed by OpenAI.
1 code implementation • 29 May 2023 • Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, Jimmy Xiangji Huang
The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently.
Ranked #8 on Natural Language Inference on ANLI test
no code implementations • 28 May 2023 • Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Mahsa Azizi, Shashi Bhushan, Simon Corston-Oliver
In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise.
1 code implementation • 26 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.
no code implementations • 31 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.
no code implementations • 2 Nov 2022 • Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Shashi Bhushan TN
Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc.
no code implementations • 24 Oct 2022 • Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shayna Gardiner, Pooja Hiranandani, Shashi Bhushan TN
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text.
1 code implementation • 10 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.
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
no code implementations • NAACL (ACL) 2022 • Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan TN, Simon Corston-Oliver
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base.
1 code implementation • 22 Dec 2021 • Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang
In this paper, we address this challenge by exploring a series of domain adaptation techniques.
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
no code implementations • 27 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.
1 code implementation • 14 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.
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.
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.