Multidimensional Impacts of Generative AI and an In‐Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society
Bibliographic record
Abstract
This study extensively examines the tremendous achievements that large language models (LLMs) and generative artificial intelligence (AI) have achieved in both technical and social domains. Generative AI was a significant advancement in artificial intelligence. LLMs, members of the generative AI subclass, have altered human–robot interaction thanks to their outstanding reading and writing skills. This research focuses on how these technologies affect healthcare, the economy, education, and ethics. The objective is to investigate LLM's real-world use in a variety of industries while also examining the most recent research and case studies. We are most concerned with the effectiveness, efficiency, and ethical implications of these procedures. The procedure also includes surveying and interviewing experts to gain a better understanding of LLMs’ real-world applications and challenges. Here are a few examples of how language translation, content production, and data analysis have enhanced LLM efficiency and accuracy. Unfortunately, concerns about discrimination, privacy, and misuse persisted. The research demonstrates the versatility of LLMs by applying them to atypical fields like mental health care and personalized education. LLMs and generative AI offer enormous promise for advancing society and technology. Addressing their moral and practical problems is critical, despite their tremendous benefits. The goal of this research is to find a happy medium in terms of LLM use. Focusing on research, ethics, and ethical use may help enhance their potential while minimizing their risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".