5 citations · 5 across the 6 of their papers we have counts for
6 papers
BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences?
Soumadeep Saha, Krish Sharma, Akshay Chaturvedi +1
Although reinforcement learning with verifiable rewards (RLVR) has improved the performance of large language models (LLMs) across a variety of reasoning tasks, there is significan…
TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability
Krish Sharma, Omar Naim, Soumadeep Saha +3
Recent work has promoted task-aware layer pruning as a way to improve model performance on particular tasks, as shown by TALE. In this paper, we investigate when such improvements…
TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination
Omar Naim, Krish Sharma, Niyar R Barman +1
Large Language Models (LLMs) typically come with a fixed architecture, despite growing evidence that not all layers contribute equally to every downstream task. We introduce TALE (…
Analyzing limits for in-context learning
Omar Naim, Jerome Bolte, Nicholas Asher
Our paper challenges claims from prior research that transformer-based models, when learning in context, implicitly implement standard learning algorithms. We present empirical evi…
Re-examining learning linear functions in context
Omar Naim, Guilhem Fouilhé, Nicholas Asher
In-context learning (ICL) has emerged as a powerful paradigm for easily adapting Large Language Models (LLMs) to various tasks. However, our understanding of how ICL works remains…
On Explaining with Attention Matrices
Omar Naim, Nicholas Asher
This paper explores the much discussed, possible explanatory link between attention weights (AW) in transformer models and predicted output. Contrary to intuition and early researc…