Summary of Challenges in Guardrailing Large Language Models For Science, by Nishan Pantha et al.
Challenges in Guardrailing Large Language Models for Science
by Nishan Pantha, Muthukumaran Ramasubramanian, Iksha Gurung, Manil Maskey, Rahul Ramachandran
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper presents a comprehensive framework for deploying large language models (LLMs) in the scientific domain to address critical failure modes related to scientific integrity and trustworthiness. The authors identify specific challenges such as time sensitivity, knowledge contextualization, conflict resolution, and intellectual property concerns that necessitate unique guardrails for LLMs applied to scientific research. The proposed guideline framework includes dimensions of trustworthiness, ethics & bias, safety, and legal aspects. The implementation strategies involve white-box, black-box, and gray-box methodologies that can be enforced within scientific contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about how large language models can sometimes get it wrong when used in scientific research. These models are super powerful and have many uses, but they need special rules to follow so that the results are trustworthy and safe. The researchers identify some major challenges with using these models in science, like making sure the information is up-to-date, relevant, and not biased. They then propose a set of guidelines for how to use these models safely and correctly in scientific research. |
