How to mitigate or reduce risks of being exposed to environmental issues for Large language models?
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To mitigate or reduce risks of being exposed to environmental issues for Large Language Models (LLMs), several approaches have been proposed. One approach is to use watermarking techniques to embed signals into generated text that are invisible to humans but detectable algorithmically . This allows for the detection of LLM-generated content and helps in identifying potential misinformation . Another strategy is to explore defensive strategies such as prompting, misinformation detection, and majority voting to mitigate the harm caused by LLM-generated misinformation . Additionally, interdisciplinary collaboration and further research are needed to address the challenge of LLM-generated misinformation and promote responsible use of LLMs .
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| Papers (5) | Insight |
|---|---|
2 Citations | The provided paper does not discuss how to mitigate or reduce risks of being exposed to environmental issues for Large Language Models. The paper focuses on the potential misuse of LLMs for generating misinformation and its impact on information-intensive applications. |
23 May 2023 | The provided paper does not discuss the mitigation or reduction of risks of being exposed to environmental issues for Large Language Models. |
| The provided paper does not discuss how to mitigate or reduce risks of being exposed to environmental issues for large language models. | |
| The provided paper does not discuss how to mitigate or reduce risks of being exposed to environmental issues for large language models. | |
15 Jun 2023 | The provided paper does not discuss how to mitigate or reduce risks of being exposed to environmental issues for large language models. The paper focuses on the combination of automation of code generation using large language models with scalable defect elimination methods using symbolic AI. |
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