5 citations · 9 across the 10 of their papers we have counts for
6 papers · 1 filter
Decoder-based Sense Knowledge Distillation
Qitong Wang, Mohammed J. Zaki, Georgios Kollias +1
Large language models (LLMs) learn contextual embeddings that capture rich semantic information, yet they often overlook structured lexical knowledge such as word senses and relati…
Multi-Sense Embeddings for Language Models and Knowledge Distillation
Qitong Wang, Mohammed J. Zaki, Georgios Kollias +1
Transformer-based large language models (LLMs) rely on contextual embeddings which generate different (continuous) representations for the same token depending on its surrounding c…
Can Memory-Augmented Language Models Generalize on Reasoning-in-a-Haystack Tasks?
Payel Das, Ching-Yun Ko, Sihui Dai +3
Large language models often expose their brittleness in reasoning tasks, especially while executing long chains of reasoning over context. We propose MemReasoner, a new and simple…
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts
Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan +6
Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a signif…
Generation Constraint Scaling Can Mitigate Hallucination
Georgios Kollias, Payel Das, Subhajit Chaudhury
Addressing the issue of hallucinations in large language models (LLMs) is a critical challenge. As the cognitive mechanisms of hallucination have been related to memory, here we ex…
Needle in the Haystack for Memory Based Large Language Models
Elliot Nelson, Georgios Kollias, Payel Das +2
Current large language models (LLMs) often perform poorly on simple fact retrieval tasks. Here we investigate if coupling a dynamically adaptable external memory to a LLM can allev…