activity
20192024
most citedOpen Information Extraction from Question-Answer Pairs

5 citations · 16 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CL2024★ 2 cited

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…

cs.CL2023

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…

cs.CL2023★ 2 cited

XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates

Haopeng Zhang, Hayate Iso, Sairam Gurajada +1

Text editing is a crucial task of modifying text to better align with user intents. However, existing text editing benchmark datasets contain only coarse-grained instructions and l…

cs.CL2023★ 2 cited

Less is More for Long Document Summary Evaluation by LLMs

Yunshu Wu, Hayate Iso, Pouya Pezeshkpour +2

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 prob…

cs.CL2023★ 4 cited

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…

cs.CL2022★ 1 cited

Zero-shot Triplet Extraction by Template Infilling

Bosung Kim, Hayate Iso, Nikita Bhutani +3

The task of triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. Most existing methods train an extraction model on traini…

cs.CL2022

CompactIE: Compact Facts in Open Information Extraction

Farima Fatahi Bayat, Nikita Bhutani, H. V. Jagadish

A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This sev…

cs.CL2021

Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?

Sagnik Ray Choudhury, Nikita Bhutani, Isabelle Augenstein

There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language mo…

cs.CL2021

Powering Effective Climate Communication with a Climate Knowledge Base

Kameron B. Rodrigues, Shweta Khushu, Mukut Mukherjee +4

While many accept climate change and its growing impacts, few converse about it well, limiting the adoption speed of societal changes necessary to address it. In order to make effe…

cs.CL2020

SubjQA: A Dataset for Subjectivity and Review Comprehension

Johannes Bjerva, Nikita Bhutani, Behzad Golshan +2

Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-…

cs.CL2019★ 5 cited

Open Information Extraction from Question-Answer Pairs

Nikita Bhutani, Yoshihiko Suhara, Wang-Chiew Tan +2

Open Information Extraction (OpenIE) extracts meaningful structured tuples from free-form text. Most previous work on OpenIE considers extracting data from one sentence at a time.…