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Md Tahmid Rahman Laskar

Md Tahmid Rahman Laskar has 5 papers in our library, in 2022–2026, most of them on Evaluation & benchmarks, Summarization, Language models & pretraining; often with Enamul Hoque, Mizanur Rahman, Shafiq Joty.

5 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 16 · 88 works · 1,009 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 193 times in our library · ⭐ Paper Stars ranking: #4283

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓York UniversityToronto · Canada2018–2026 · 28 paperscurrent🎓York UniversityYork · United States2022–2025 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 3 works

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 3 worksSummarization · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20241 paper20232 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets (2023 · 200 citations)
    When ChatGPT appeared, nobody had tested it systematically on standard academic NLP benchmarks. Its free-form conversational answers are hard to score automatically against gold labels. The authors ran a broad zero-shot evaluation covering 140 benchmark datasets and about 255K generated responses. The tasks included…
  2. A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024 · 105 citations)
    LLM evaluation results often conflict across studies because each study sets up its experiments differently.
  3. Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization (2022 · 76 citations)
    Query-focused abstractive summarization (QFAS) means generating a summary of one or more documents that answers a specific query.
  4. ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries (2023 · 30 citations)
    Turning charts into natural-language descriptions helps visually impaired readers and supports information retrieval.
  5. Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization (2026)
    Text-to-visualization (Text2Vis) means taking a data table and a natural-language question and producing both a short textual answer and executable plotting code.

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 193 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated

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