9 citations · 18 across the 5 of their papers we have counts for
5 papers
Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions
Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani +3
Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards…
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks
Pouya Pezeshkpour, Hayate Iso, Thom Lake +2
Numerous HR applications are centered around resumes and job descriptions. While they can benefit from advancements in NLP, particularly large language models, their real-world ado…
Rethinking Language Models as Symbolic Knowledge Graphs
Vishwas Mruthyunjaya, Pouya Pezeshkpour, Estevam Hruschka +1
Symbolic knowledge graphs (KGs) play a pivotal role in knowledge-centric applications such as search, question answering and recommendation. As contemporary language models (LMs) t…
Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions
Pouya Pezeshkpour, Estevam Hruschka
Large Language Models (LLMs) have demonstrated remarkable capabilities in various NLP tasks. However, previous works have shown these models are sensitive towards prompt wording, a…
Measuring and Modifying Factual Knowledge in Large Language Models
Pouya Pezeshkpour
Large Language Models (LLMs) store an extensive amount of factual knowledge obtained from vast collections of text. To effectively utilize these models for downstream tasks, it is…