no code implementations • 23 May 2024 • Sabri Boughorbel, Md Rizwan Parvez, Majd Hawasly
We translate TinyStories, a dataset of 2. 2M short stories for 3-4 year old children, from English to Arabic using the free NLLB-3B MT model.
1 code implementation • 18 May 2024 • Md. Ashraful Islam, Mohammed Eunus Ali, Md Rizwan Parvez
In this paper, we introduce a new approach to code generation tasks leveraging multi-agent prompting that uniquely replicates the full cycle of program synthesis as observed in human developers.
no code implementations • 14 Mar 2024 • Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty
Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.
no code implementations • 11 Jan 2024 • Md Rizwan Parvez
Instead of unverified reasoning claims, this innovative approach leverages the power of "evidence for decision making" by first focusing exclusively on the thought sequences (the series of intermediate steps) explicitly mentioned in the context which then serve as extracted evidence, guiding the LLM's output generation process with greater precision and efficiency.
no code implementations • 8 Dec 2023 • Mobashir Sadat, Zhengyu Zhou, Lukas Lange, Jun Araki, Arsalan Gundroo, Bingqing Wang, Rakesh R Menon, Md Rizwan Parvez, Zhe Feng
Hallucination is a well-known phenomenon in text generated by large language models (LLMs).
1 code implementation • 14 Nov 2023 • Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, Graham Neubig
To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time.
3 code implementations • 6 Mar 2023 • Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, Weishi Wang, Md Rizwan Parvez, Shafiq Joty
Recently, pre-trained large language models (LLMs) have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments.
no code implementations • 19 Apr 2022 • Md Rizwan Parvez, Jianfeng Chi, Wasi Uddin Ahmad, Yuan Tian, Kai-Wei Chang
Prior studies in privacy policies frame the question answering (QA) task as identifying the most relevant text segment or a list of sentences from a policy document given a user query.
1 code implementation • Findings (EMNLP) 2021 • Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models.
Ranked #1 on Code Generation on CodeXGLUE - CodeSearchNet (using extra training data)
1 code implementation • NAACL 2021 • Md Rizwan Parvez, Kai-Wei Chang
Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP).