REVIEW 7 cited by
LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and why. We present the first large-scale survey of 816 verified research article authors to understand how the research community leverages and perceives LLMs as research tools. We examine participants' self-reported LLM usage, finding that 81% of researchers have already incorporated LLMs into different aspects of their research workflow. We also find that traditionally disadvantaged groups in academia (non-White, junior, and non-native English speaking researchers) report higher LLM usage and perceived benefits, suggesting potential for improved research equity. However, women, non-binary, and senior researchers have greater ethical concerns, potentially hindering adoption.
Forward citations
Cited by 7 Pith papers
-
Whose Name Comes Up? II: Benchmarking and Intervention-Based Auditing of LLM-Based Scholar Recommendation
A new benchmark auditing LLM-based scholar recommendation across 22 models shows that inference-time interventions (temperature, constrained prompting, RAG) trade off technical quality against social representation ra...
-
Authorship Without Writing: Large Language Models and the Senior Author Analogy
A human who directs, critically reviews, approves, and takes responsibility for an LLM-generated paper can legitimately count as its author, by the same criteria that make non-writing senior researchers authors.
-
Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing
AI disclosure lowers perceived article quality for both human and LLM raters, and only LLM raters show a demographic preference that disappears when AI assistance is disclosed.
-
Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning
Most adults in a German Prolific sample report using large language models for informal learning, with four distinct learner profiles emerging from their usage patterns.
-
Human-LLM Coevolution: Evidence from Academic Writing
After ChatGPT-style words were publicly flagged in early 2024, their frequency in arXiv abstracts dropped, while other common LLM-favored words kept rising, suggesting authors are adapting their writing to avoid detection.
-
MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling
Concentrating synthetic preference paraphrases on low-margin pairs gives consistent but small reward-model and alignment gains in single-run experiments, while the abstract's semantic-aware, multi-benchmark claims are...
-
Conversational AI for Rapid Scientific Prototyping: A Case Study on ESA's ELOPE Competition
One engineer paired with ChatGPT and reached second place in ESA's ELOPE competition in about one week of work; the paper draws best-practice lessons from that experience.
Discussion (0). Continue with ORCID to comment.