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[R] Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4 (alien.top)

submitted 1 year ago by KellinPelrine@alien.top to c/machinelearning@academy.garden

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Paper: https://arxiv.org/abs/2305.14928

Kellin Pelrine, Anne Imouza, Camille Thibault, Meilina Reksoprodjo, Caleb Gupta, Joël Christoph, Jean-François Godbout, Reihaneh Rabbany

Greatly updated version of our misinformation mitigation paper, forthcoming at EMNLP 2023, is out!

We propose 3 key elements for building a reliable misinfo mitigation system: recent LLMs, graceful failure, and a focus on generalization.

In addition to results on each of those, we conducted experiments on temperature, prompting, comparing LLMs, versioning, explainability, web retrieval, and more. We also published the LIAR-New dataset, which has every example in both English and French, plus novel Possibility labels which mark whether an input has sufficient context or is too ambiguous for veracity evaluation.

Please check it out! And I'd be happy to hear your thoughts.

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[–] CatalyzeX_code_bot@alien.top 1 points 1 year ago

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[–] currentscurrents@alien.top 1 points 1 year ago

As much as I agree misinformation is a problem, automatic misinformation detection systems seem only one software update away from automatic censorship systems.

Whoever built the system can decide what truth is, and what ideas are allowed to be discussed. And hey, maybe you agree with them. But what if somebody like Elon Musk or Peter Thiel buys out your social media company? I certainly don't agree with their ideas of reality.

I don't think there can be a systemic solution to misinformation that doesn't compromise free speech. It's the frying pan or the fire.