1 citations · 2 across the 4 of their papers we have counts for
5 papers
Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation
Joel Dag, Nikit Srivastava, Hamada M. Zahera +1
Multilingual models share capacity across languages, so adding languages can hurt performance on existing ones. Mixture-of-experts models expand this capacity with little additiona…
MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA
Nikit Srivastava, Daniel Vollmers, René Speck +3
Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks requiring up-to…
Explainable Benchmarking through the Lense of Concept Learning
Quannian Zhang, Michael Röder, Nikit Srivastava +2
Evaluating competing systems in a comparable way, i.e., benchmarking them, is an undeniable pillar of the scientific method. However, system performance is often summarized via a s…
LOLA -- An Open-Source Massively Multilingual Large Language Model
Nikit Srivastava, Denis Kuchelev, Tatiana Moteu Ngoli +5
This paper presents LOLA, a massively multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. Our architect…
MST5 -- Multilingual Question Answering over Knowledge Graphs
Nikit Srivastava, Mengshi Ma, Daniel Vollmers +3
Knowledge Graph Question Answering (KGQA) simplifies querying vast amounts of knowledge stored in a graph-based model using natural language. However, the research has largely conc…