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Found 10 papers, 3 papers with code
Mapping the Increasing Use of LLMs in Scientific Papers
To address this gap, we conduct the first systematic, large-scale analysis across 950, 965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time.
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM).
The Rise of Open Science: Tracking the Evolution and Perceived Value of Data and Methods Link-Sharing Practices
In recent years, funding agencies and journals increasingly advocate for open science practices (e. g. data and method sharing) to improve the transparency, access, and reproducibility of science.
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
We first quantitatively compared GPT-4's generated feedback with human peer reviewer feedback in 15 Nature family journals (3, 096 papers in total) and the ICLR machine learning conference (1, 709 papers).
User Experience Design Professionals' Perceptions of Generative Artificial Intelligence
They emphasized the unique human factors of "enjoyment" and "agency", where humans remain the arbiters of "AI alignment".
Comparing scalable strategies for generating numerical perspectives
Numerical perspectives help people understand extreme and unfamiliar numbers (e. g., \$330 billion is about \$1, 000 per person in the United States).
Evaluating Human-Language Model Interaction
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e. g., consumption, socialization, and entertainment.
My Team Will Go On: Differentiating High and Low Viability Teams through Team Interaction
From these models, we identify the use of exclusive language such as `but' and `except', and the use of second person pronouns, as the most predictive features for detecting the most viable teams, suggesting that active engagement with others' ideas is a crucial signal of a viable team.
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora
We extract scientific concepts (i. e., phrases) from corpora as instantiations of "research ideas", create concept-level features as motivated by literature, and then follow the trajectories of over 450, 000 new concepts (emerged from 1995-2014) to identify factors that lead only a small proportion of these ideas to be used in inventions and drug trials.
