Zobrazeno 1 - 10
of 1 609
pro vyhledávání: '"P. Blodgett"'
Gaps Between Research and Practice When Measuring Representational Harms Caused by LLM-Based Systems
Autor:
Harvey, Emma, Sheng, Emily, Blodgett, Su Lin, Chouldechova, Alexandra, Garcia-Gathright, Jean, Olteanu, Alexandra, Wallach, Hanna
To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has produced and made publicly available numerous measurement instruments, including tools, datasets, metrics, benc
Externí odkaz:
http://arxiv.org/abs/2411.15662
Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We
Externí odkaz:
http://arxiv.org/abs/2411.13032
Autor:
Wallach, Hanna, Desai, Meera, Pangakis, Nicholas, Cooper, A. Feder, Wang, Angelina, Barocas, Solon, Chouldechova, Alexandra, Atalla, Chad, Blodgett, Su Lin, Corvi, Emily, Dow, P. Alex, Garcia-Gathright, Jean, Olteanu, Alexandra, Reed, Stefanie, Sheng, Emily, Vann, Dan, Vaughan, Jennifer Wortman, Vogel, Matthew, Washington, Hannah, Jacobs, Abigail Z.
Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of meas
Externí odkaz:
http://arxiv.org/abs/2411.10939
Many state-of-the-art generative AI (GenAI) systems are increasingly prone to anthropomorphic behaviors, i.e., to generating outputs that are perceived to be human-like. While this has led to scholars increasingly raising concerns about possible nega
Externí odkaz:
http://arxiv.org/abs/2410.08526
Cooling atoms to the ground-state of optical tweezers is becoming increasingly important for high-fidelity imaging, cooling, and molecular assembly. While extensive theoretical work has been conducted on cooling in free space, fewer studies have focu
Externí odkaz:
http://arxiv.org/abs/2406.19153
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
Longstanding data labeling practices in machine learning involve collecting and aggregating labels from multiple annotators. But what should we do when annotators disagree? Though annotator disagreement has long been seen as a problem to minimize, ne
Externí odkaz:
http://arxiv.org/abs/2405.05860
As machine learning applications proliferate, we need an understanding of their potential for harm. However, current fairness metrics are rarely grounded in human psychological experiences of harm. Drawing on the social psychology of stereotypes, we
Externí odkaz:
http://arxiv.org/abs/2402.04420
Autor:
Liu, Yu Lu, Cao, Meng, Blodgett, Su Lin, Cheung, Jackie Chi Kit, Olteanu, Alexandra, Trischler, Adam
AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work might engender. However, for specific NLP tasks our understanding of how
Externí odkaz:
http://arxiv.org/abs/2311.11103
Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to behave identically for social groups, to adaptation, where behaviors should instead vary across them. To illumina
Externí odkaz:
http://arxiv.org/abs/2310.15398