Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Liao, Q. Vera"'
Autor:
Liu, Yu Lu, Blodgett, Su Lin, Cheung, Jackie Chi Kit, Liao, Q. Vera, Olteanu, Alexandra, Xiao, Ziang
Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which datasets to include, which metrics to use) that often rely on tacit, untested assumptions about what the benchmar
Externí odkaz:
http://arxiv.org/abs/2406.08723
Autor:
Kim, Sunnie S. Y., Liao, Q. Vera, Vorvoreanu, Mihaela, Ballard, Stephanie, Vaughan, Jennifer Wortman
Widely deployed large language models (LLMs) can produce convincing yet incorrect outputs, potentially misleading users who may rely on them as if they were correct. To reduce such overreliance, there have been calls for LLMs to communicate their unc
Externí odkaz:
http://arxiv.org/abs/2405.00623
Large language models (LLMs) powered conversational search systems have already been used by hundreds of millions of people, and are believed to bring many benefits over conventional search. However, while decades of research and public discourse int
Externí odkaz:
http://arxiv.org/abs/2402.05880
Autor:
Feng, K. J. Kevin, Liao, Q. Vera, Xiao, Ziang, Vaughan, Jennifer Wortman, Zhang, Amy X., McDonald, David W.
Advancements in large language models (LLMs) are sparking a proliferation of LLM-powered user experiences (UX). In product teams, designers often craft UX to meet user needs, but it is unclear how they engage with LLMs as a novel design material. Thr
Externí odkaz:
http://arxiv.org/abs/2401.09051
The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications a
Externí odkaz:
http://arxiv.org/abs/2306.01941
Autor:
Liao, Q. Vera, Xiao, Ziang
The recent development of generative and large language models (LLMs) poses new challenges for model evaluation that the research community and industry are grappling with. While the versatile capabilities of these models ignite excitement, they also
Externí odkaz:
http://arxiv.org/abs/2306.03100
We address a fundamental challenge in Natural Language Generation (NLG) model evaluation -- the design and evaluation of evaluation metrics. Recognizing the limitations of existing automatic metrics and noises from how current human evaluation was co
Externí odkaz:
http://arxiv.org/abs/2305.14889
Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when working with large datasets. While recent AI-based tools demonstrate uti
Externí odkaz:
http://arxiv.org/abs/2304.10548
Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining a
Externí odkaz:
http://arxiv.org/abs/2304.08366
Autor:
Kawakami, Anna, Chowdhary, Shreya, Iqbal, Shamsi T., Liao, Q. Vera, Olteanu, Alexandra, Suh, Jina, Saha, Koustuv
With the heightened digitization of the workplace, alongside the rise of remote and hybrid work prompted by the pandemic, there is growing corporate interest in using passive sensing technologies for workplace wellbeing. Existing research on these te
Externí odkaz:
http://arxiv.org/abs/2303.06794