Papers by Nishanth Dikkala
On the Benefits of Learning to Route in Mixture-of-Experts Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing Mixture-of-Expert (MoE) models allow us to scale up model sizes while keeping the amount of compute time fixed. |
| Approach: | They propose to use a router to route inputs to experts in a layer to scale up model sizes while keeping the amount of compute time fixed. |
| Outcome: | The proposed model scales up with the help of a router that routes input tokens to experts in a layer and shows that it is more efficient than a non-trainable router. |
BIG-Bench Extra Hard (2025.acl-long)
Copied to clipboard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat
| Challenge: | Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. |
| Approach: | They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning . |
| Outcome: | The new benchmark pushes the boundaries of LLM reasoning evaluation. |