--- title: "Siddhartha Brahma — author profile and papers" description: "Siddhartha Brahma: 2 papers on Elamur Research (2022–2023), h-index 13 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/siddhartha-brahma/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/siddhartha-brahma/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Siddhartha Brahma # Siddhartha Brahma Siddhartha Brahma's papers here concern large language models and how they learn to follow instructions. They ask how models can be trained to handle tasks they have not seen, and how instruction-following can be measured reliably. The methods include finetuning pretrained models on broad mixtures of instruction-phrased tasks and designing benchmarks whose outcomes can be checked automatically. This places the work within [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [evaluation and benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/). The most cited of these papers is [Scaling Instruction-Finetuned Language Models](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/) from 2022. It extends the Flan procedure along three axes: the number of training tasks, the size of the model, and the inclusion of step-by-step reasoning examples. The approach is applied to T5, PaLM and U-PaLM at a wide range of scales to see how each axis affects generalization. The second work, [Instruction-Following Evaluation for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/) from 2023, introduces IFEval. This benchmark is built around "verifiable instructions," constraints such as length limits, letter case or output format that a simple program can test. It avoids the cost of human raters and the biases of model-based judges. The newest work thus moves from training models to follow instructions toward checking whether they actually do. Bylines from 2020 to 2023 list [Google](https://md.research.elamur.ai/free/en/companies/google/). Earlier papers list [IBM Research - Almaden](https://md.research.elamur.ai/free/en/labs/ibm-research-almaden/) and, from 2011 to 2016, [École Polytechnique Fédérale de Lausanne](https://md.research.elamur.ai/free/en/universities/ecole-polytechnique-federale-de-lausanne/). Co-authors include [Le Hou](https://md.research.elamur.ai/free/en/authors/le-hou/), [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/) and [Denny Zhou](https://md.research.elamur.ai/free/en/authors/denny-zhou/), along with others on the Flan scaling study. Siddhartha Brahma has 2 papers in our library, in 2022–2023, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Le Hou](https://md.research.elamur.ai/free/en/authors/le-hou/), [Albert Webson](https://md.research.elamur.ai/free/en/authors/albert-webson/), [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/). 2 papers in our library · 2022–2023 [OpenAlex](https://openalex.org/A5047714914) · [**h-index 13**](https://openalex.org/authors/A5047714914) · [43 works](https://openalex.org/works?filter=author.id:A5047714914) · [2,779 citations](https://openalex.org/works?filter=author.id:A5047714914&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/siddhartha-brahma/#papers) · [🔗 6,942 citations in our library](https://md.research.elamur.ai/free/en/authors/siddhartha-brahma/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Star rank #914](https://md.research.elamur.ai/free/en/paper-stars/page/10/#star-914) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Siddhartha+Brahma) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Siddhartha+Brahma) · [dblp](https://dblp.org/search?q=Siddhartha+Brahma) · [ACL Anthology](https://aclanthology.org/search/?q=Siddhartha+Brahma) ## Affiliations [🎓**École Polytechnique Fédérale de Lausanne** Lausanne · Switzerland 2011–2016 · 14 papers](https://md.research.elamur.ai/free/en/universities/ecole-polytechnique-federale-de-lausanne/) · [🏢**Google** Mountain View · United States 2020–2023 · 7 papers](https://md.research.elamur.ai/free/en/companies/google/) · [🔬**IBM Research - Almaden** San Jose · United States 2018–2019 · 4 papers](https://md.research.elamur.ai/free/en/labs/ibm-research-almaden/) · [🎓**University of Neuchâtel** Neuchâtel · Switzerland 2015–2016 · 3 papers](https://ror.org/00vasag41) · [🎓**Indian Institute of Technology Kharagpur** Kharagpur · India 2004 · 2 papers](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-kharagpur/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Topics [Language models & pretraining (2)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Evaluation & benchmarks (1)](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) ## Works by year ### 2020s [**2023** 1 paper](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/) ## Papers The most cited first (OpenAlex and our library). 1. **[Scaling Instruction-Finetuned Language Models](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/)** *(2022 · 2,176 citations)* The authors from Google seek a way to teach pretrained language models to better handle tasks they have not seen before. 2. **[Instruction-Following Evaluation for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/)** *(2023 · 586 citations)* Judging whether an LLM actually obeys instructions is hard to standardize. Human raters are costly, slow and inconsistent, and an LLM acting as judge brings its own biases and blind spots. The authors propose IFEval, a benchmark limited to "verifiable instructions": constraints that a short deterministic program can… ## Also in our library 1 more work of theirs is in our library without a page here yet; it will appear as we publish them. ## Co-authors here 12 of 14 co-authors with a page, the most papers together first. - [Le Hou](https://md.research.elamur.ai/free/en/authors/le-hou/) ×2 - [Albert Webson](https://md.research.elamur.ai/free/en/authors/albert-webson/) - [Barret Zoph](https://md.research.elamur.ai/free/en/authors/barret-zoph/) - [Denny Zhou](https://md.research.elamur.ai/free/en/authors/denny-zhou/) - [Hyung Won Chung](https://md.research.elamur.ai/free/en/authors/hyung-won-chung/) - [Mostafa Dehghani](https://md.research.elamur.ai/free/en/authors/mostafa-dehghani/) - [Shayne Longpre](https://md.research.elamur.ai/free/en/authors/shayne-longpre/) - [Shixiang Shane Gu](https://md.research.elamur.ai/free/en/authors/shixiang-shane-gu/) - [Swaroop Mishra](https://md.research.elamur.ai/free/en/authors/swaroop-mishra/) - [William Fedus](https://md.research.elamur.ai/free/en/authors/william-fedus/) - [Xuezhi Wang](https://md.research.elamur.ai/free/en/authors/xuezhi-wang/) - [Yi Luan](https://md.research.elamur.ai/free/en/authors/yi-luan/) ## Cited by Papers of our library cite their works 6,942 times (of the 140,884 papers whose bibliographies we read); [422 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/siddhartha-brahma/cited-by/).