Mohammed Muqeeth

Mohammed Muqeeth has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining; often with Colin Raffel, Haokun Liu, Derek Tam.

2 papers in our library · 2022–2023

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

The most cited first (OpenAlex and our library).

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022 · 754 citations)
Few-shot in-context learning (ICL) lets a large language model take on a new task without training. However, every prediction must reprocess all demonstration examples, which is costly, and results shift with prompt wording and example order. The authors test whether parameter-efficient fine-tuning (PEFT) is a better…
Soft Merging of Experts with Adaptive Routing (2023 · 24 citations)
Models that route inputs to expert subnetworks promise modularity, but routers that make discrete expert choices often perform worse than parameter-matched dense models or simple hand-designed routing rules.

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

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

Papers of our library cite their works 401 times (of the 149,439 papers whose bibliographies we read); 83 citing papers with a page here →.

Updated 3 November 2026