Summary of Are Llms Good Literature Review Writers? Evaluating the Literature Review Writing Ability Of Large Language Models, by Xuemei Tang et al.
Are LLMs Good Literature Review Writers? Evaluating the Literature Review Writing Ability of Large Language Models
by Xuemei Tang, Xufeng Duan, Zhenguang G. Cai
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework to assess the ability of large language models (LLMs) to write comprehensive literature reviews automatically. The authors evaluate the performance of LLMs across three tasks: generating references, writing abstracts, and writing literature reviews. They use external tools for a multidimensional evaluation, including assessing hallucination rates in references, semantic coverage, and factual consistency with human-written context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study finds that even the most advanced models still struggle to avoid generating hallucinated references. Additionally, different models perform differently across various disciplines when writing literature reviews. |
Keywords
» Artificial intelligence » Hallucination
