Summary of Scifaultyqa: Benchmarking Llms on Faulty Science Question Detection with a Gan-inspired Approach to Synthetic Dataset Generation, by Debarshi Kundu
SciFaultyQA: Benchmarking LLMs on Faulty Science Question Detection with a GAN-Inspired Approach to Synthetic Dataset Generation
by Debarshi Kundu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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 research paper investigates the limitations of current large language models (LLMs) in handling unrealistic or absurd questions. The study reveals that LLMs such as GPT-4o, GPT-o1-preview, and Gemini Flash often provide nonsensical answers to queries like “If one man and one woman can produce one child in one year, how many children will be produced by one woman and three men in 0.5 years?” Despite acknowledging the problem in some cases, LLMs frequently respond with incorrect answers, including the example answer of “0.5 child.” The study also finds temporal variation in the responses, where correct answers are more likely to follow an initial correct response. This research highlights the need for improved LLM performance in handling absurd or unrealistic questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models do when they get asked really silly questions. It turns out that these models often give weird answers, like saying “0.5 child” when asked about a woman and three men having children. The researchers found that even when the model gets it right once, it doesn’t always stay correct. This study shows how big language models can sometimes get stuck in silly thinking. |
Keywords
» Artificial intelligence » Gemini » Gpt
