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20032026
most citedFactuality Challenges in the Era of Large Language Models

163 citations · 464 across the 29 of their papers we have counts for

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Showing 2023 · cs.CLShow all

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cs.CL2023★ 163 cited

Factuality Challenges in the Era of Large Language Models

Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha +15

The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incre…

cs.CL2023★ 1 cited

TimelineQA: A Benchmark for Question Answering over Timelines

Wang-Chiew Tan, Jane Dwivedi-Yu, Yuliang Li +4

Lifelogs are descriptions of experiences that a person had during their life. Lifelogs are created by fusing data from the multitude of digital services, such as online photos, map…

cs.CL2023

Reimagining Retrieval Augmented Language Models for Answering Queries

Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez +4

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where mo…

cs.CL2023

NormBank: A Knowledge Bank of Situational Social Norms

Caleb Ziems, Jane Dwivedi-Yu, Yi-Chia Wang +2

We present NormBank, a knowledge bank of 155k situational norms. This resource is designed to ground flexible normative reasoning for interactive, assistive, and collaborative AI s…

cs.CL2023★ 3 cited

Learnings from Data Integration for Augmented Language Models

Alon Halevy, Jane Dwivedi-Yu

One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data. As a result, there are multiple efforts to extend langu…