Large language models (LLMs) are widely used to assist writing, but this study shows they alter both tone and meaning of human text. A user study found that heavy LLM use increased neutral essays by nearly 70% and made writers feel less creative and less in their voice. Even when prompted to make only grammar edits, LLMs changed the semantic content of essays and produced AI-generated scientific reviews that were less focused on clarity and significance and scored higher on average.
By Marwa Abdulhai, Isadora White, Yanming Wan, Ibrahim Qureshi, Joel Z. Leibo, Max Kleiman-Weiner, Natasha Jaques
arXiv:2601.17036v2 Announce Type: replace-cross
Abstract: ArXiv recently prohibited the upload of unpublished review papers to its servers in the Computer Science domain, citing a high prevalence of...
By Yanai Elazar, Maria Antoniak
arXiv:2603. 20450v2 Announce Type: replace-cross Abstract: A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews.
By Rounak Saha, Gurusha Juneja, Dayita Chaudhuri, Naveeja Sajeevan, Nihar B Shah, Danish Pruthi
arXiv:2608. 06589v1 Announce Type: cross Abstract: While large language model outputs are frequently analysed as a collective super variety termed "AI language," this chapter argues that this perspective coexists with distinct, model-specific linguistic signatures akin to human idiolects.
By Karolina Rudnicka, Thomas Stephan Juzek
arXiv:2506. 17467v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society.
By Weixin Liang
The paper investigates how large language models can extract contextualized data from scientific literature. It presents four workflows: expert‑written prompts, self‑generated prompts, autonomous literature discovery, and dataset creation from guidelines. While models perform well with prompts, they struggle with context, hallucinate references, and still need human oversight for final validation.
By Valentin Romanov, Monique Bax, Steven Niederer
arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.
By Yangjun Lu, Hongyi Zhou, Fabian Spill, Kai Ye, Chengchun Shi, Jin Zhu
Drift Inspector is an open‑source system that extracts Atomic Contribution Claims (ACCs) from scientific abstracts using an LLM, then clusters these claims over time to map how a research field evolves. Applied to six years of EMNLP, the tool reveals a shift from classic NLP tasks toward LLM‑era capabilities such as reasoning and multimodality—trends that keyword or whole‑abstract counts miss. The pipeline has also processed the entire ACL Anthology, yielding 346,000 claims from 80,000 abstracts across 423 venues, with human‑validated extraction and clustering aligned to an external taxonomy.
By Vsevolod Karimov, Stepan Ostarkov, Anastasia Poroshina, Anatoly Frolov, Alexander Panchenko
arXiv:2606. 00019v1 Announce Type: cross Abstract: Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear.
By Yiliang Zhou, Yawen Guo, Sairam Sutari, Jasmine Dhillon, Alexandra L. Beck, Emilie Chow, Steven Tam, Danielle Perret, Deepti Pandita, Gelareh Sadigh, Archana J. McEligot, Kai Zheng
Scientific abstracts mix contributions with background, motivation, and meta-language, so tools that read them as-is cannot separate what a field produces from what it discusses. We present Drift Insp...
arXiv:2609.01432v1 Announce Type: cross
Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentionin...
By Yixuan Liu, Lin Chen, Zhuoqi Liu, Jianglin Lu, Dakota Murray
The paper evaluates browser-based large language models (LLMs) for extracting detailed, contextualized data from scientific papers. It presents four workflows: (1) expert-curated prompts yield good extraction but struggle with nuance; (2) LLMs can generate effective prompts from simple instructions; (3) autonomous literature discovery is challenging, with missing or hallucinated references; (4) LLMs can build new datasets from guidelines that align closely with human experts, yet still need human oversight. The study outlines a practical, auditable workflow where experts set standards, models cross-check extractions, and researchers resolve disputes, enabling scalable scientific data curation.