Zobrazeno 1 - 10
of 213
pro vyhledávání: '"Pezeshkpour P"'
Large language models (LLMs) demonstrate impressive capabilities in mathematical reasoning. However, despite these achievements, current evaluations are mostly limited to specific mathematical topics, and it remains unclear whether LLMs are genuinely
Externí odkaz:
http://arxiv.org/abs/2406.05194
Autor:
Kandogan, Eser, Rahman, Sajjadur, Bhutani, Nikita, Zhang, Dan, Chen, Rafael Li, Mitra, Kushan, Gurajada, Sairam, Pezeshkpour, Pouya, Iso, Hayate, Feng, Yanlin, Kim, Hannah, Shen, Chen, Wang, Jin, Hruschka, Estevam
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goal, there is a notable shift to building compound AI systems, wherein L
Externí odkaz:
http://arxiv.org/abs/2406.00584
Autor:
Pezeshkpour, Pouya, Hruschka, Estevam
Utilizing large language models (LLMs) to rank a set of items has become a common approach in recommendation and retrieval systems. Typically, these systems focus on ordering a substantial number of documents in a monotonic order based on a given que
Externí odkaz:
http://arxiv.org/abs/2404.00211
Autor:
Pezeshkpour, Pouya, Kandogan, Eser, Bhutani, Nikita, Rahman, Sajjadur, Mitchell, Tom, Hruschka, Estevam
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 practical adoption of LLMs, multi-agent systems hold great promise to
Externí odkaz:
http://arxiv.org/abs/2402.01108
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 adoption faces challenges due to absence of comprehensive benchmarks for
Externí odkaz:
http://arxiv.org/abs/2311.06383
Large Language Models (LLMs) have shown promising performance in summary evaluation tasks, yet they face challenges such as high computational costs and the Lost-in-the-Middle problem where important information in the middle of long documents is oft
Externí odkaz:
http://arxiv.org/abs/2309.07382
Symbolic knowledge graphs (KGs) play a pivotal role in knowledge-centric applications such as search, question answering and recommendation. As contemporary language models (LMs) trained on extensive textual data have gained prominence, researchers h
Externí odkaz:
http://arxiv.org/abs/2308.13676
Autor:
Pezeshkpour, Pouya, Hruschka, Estevam
Large Language Models (LLMs) have demonstrated remarkable capabilities in various NLP tasks. However, previous works have shown these models are sensitive towards prompt wording, and few-shot demonstrations and their order, posing challenges to fair
Externí odkaz:
http://arxiv.org/abs/2308.11483
Autor:
Pezeshkpour, Pouya
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 crucial to have reliable methods for measuring their knowledge. Howeve
Externí odkaz:
http://arxiv.org/abs/2306.06264
Recently, there has been an increase in efforts to understand how large language models (LLMs) propagate and amplify social biases. Several works have utilized templates for fairness evaluation, which allow researchers to quantify social biases in th
Externí odkaz:
http://arxiv.org/abs/2210.04337