Summary of Evaluating the Performance Of Large Language Models in Scientific Claim Detection and Classification, by Tanjim Bin Faruk
Evaluating the Performance of Large Language Models in Scientific Claim Detection and Classification
by Tanjim Bin Faruk
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study evaluates the efficacy of Large Language Models (LLMs) as innovative solutions for mitigating misinformation on social media platforms like Twitter during the COVID-19 pandemic. Specifically, it assesses the performance of LLMs such as OpenAI’s GPT and Meta’s LLaMA in detecting and classifying COVID-19-related scientific claims. The study finds that LLMs have significant potential as automated fact-checking tools, though research in this domain is nascent and further exploration is required. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are being studied to see if they can help stop the spread of misinformation on social media during the COVID-19 pandemic. Right now, people are using these models to try to figure out what’s true and what’s not. The study looks at how well LLMs do this and finds that they have a lot of potential for stopping misinformation. |
Keywords
» Artificial intelligence » Gpt » Llama
