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Abstract Well-studied techniques that enhance diversity in early design concept generation require effective metrics for evaluating human-perceived similarity between ideas. Recent work suggests collecting triplet comparisons between designs directly from human raters and using those triplets to form an embedding where similarity is expressed as a Euclidean distance. While effective at modeling human-perceived similarity judgments, these methods are expensive and require a large number of triplets to be hand-labeled. However, what if there were a way to use AI to replicate the human similarity judgments captured in triplet embedding methods? In this paper, we explore the potential for pretrained Large Language Models (LLMs) to be used in this context. Using a dataset of crowdsourced text descriptions written about engineering design sketches, we generate LLM embeddings and compare them to an embedding created from human-provided triplets of those same sketches. From these embeddings, we can use Euclidean distances to describe areas where human perception and LLM perception disagree regarding design similarity. We then implement this same procedure but with descriptions written from a template that attempts to isolate a particular modality of a design (i.e., functions, behaviors, structures). By comparing the templated description embeddings to both the triplet-generated and pre-template LLM embeddings, we identify ways of describing designs such that LLM and human similarity perception better agree. We use these results to better understand how humans and LLMs interpret similarity in engineering designs.
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Combating misinformation in the age of LLMs: Opportunities and challenges
Abstract Misinformation such as fake news and rumors is a serious threat for information ecosystems and public trust. The emergence of large language models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double‐edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emerging question is:can we utilize LLMs to combat misinformation?On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is:how to combat LLM‐generated misinformation?In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions, respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM‐generated misinformation.
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- Award ID(s):
- 2241068
- PAR ID:
- 10544410
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AI Magazine
- Volume:
- 45
- Issue:
- 3
- ISSN:
- 0738-4602
- Format(s):
- Medium: X Size: p. 354-368
- Size(s):
- p. 354-368
- Sponsoring Org:
- National Science Foundation
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