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dology can significantly accelerate the discovery process 31 consequences in education edit artificial intelligence hallucinations can impact education there has been a rise in students using ai tools for assistance in research or writing tools such as grammarly or using generative ai programs such as chatgpt this has raised concern regarding academic integrity 114 in submitted projects in addition to hallucinations causing students to learn incorrect information part of this concern lies in the citations provided by llms and generative ai a 2024 study at the university of mississippi found that many of these citations that students submitted were partially or completely fabricated 47 of these sources either had incorrect titles dates authors or a combination of all 115 the study notes that these inconsistencies in student submitted citations cause educators and librarians to manually check the accuracy more frequently the journal of cranio maxillofacial surgery addresses this risk when it comes to the medical and surgical fields they mention how academic publishers have acknowledged the issue and that some journals such as jama have changed some of their policies to discourage the use of ai generated citations 116 although the journal states that a policy will not be enough to diminish the use of ai generated citations automatic tools to check the citations and ai literacy training should also be adopted instructors may use tools such as turnitin for plagiarism checking and verification of academic integrity these tools have been found to sometimes flag papers that have not used any ai assistance in their writing 117 openai has also found such a lack of accuracy in their own ai detection software that the company shut it down entirely 118 mitigation methods edit as of 2025 there is still ongoing research to try to mitigate the occurrence of hallucinations 119 research in 2022 has shown that language models not only hallucinate but also amplify hallucinations even for those which were designed to alleviate this issue 120 researchers from openai wrote that hallucinations occur because the training and evaluation of llms reward guessing over acknowledging uncertainty and proposed modifying the scoring of benchmarks 41 researchers have proposed a variety of mitigation measures including getting different chatbots to debate one another until they reach consensus on an answer 121 and neuro symbolic architectures with formal knowledge bases and inference engines 12 122 123 ji et al divide common mitigation methods into two categories data related methods and modeling and inference methods data related methods include building a faithful dataset cleaning data automatically and information augmentation by augmenting the inputs with external information model and inference methods include changes in the architecture either modifying the encoder attention or the decoder in various ways changes in the training process such as using reinforcement learning and post processing methods that can correct hallucinations in the output 124 nvidia guardrails launched in 2023 can be configured to hard code certain responses via script instead of leaving them to the llm 125 furthermore tools such as the trustworthy language model have been created to attempt to aid in the detection of hallucination 126 evaluating multiple possible replies before answering a query by assigning confidence scores to each could mitigate the problem however this approach would multiply computational costs active learning would further increase these costs in high stakes domains such as chip design supply chain logistics and medical diagnostics the added costs would be operationally necessary in order for llms to be used in chatbots however customers tend to prefer rapid overconfident answers over cautious uncertainty aware ones 127 see also edit ai alignment ai effect ai safety ai slop artifact artificial stupidity chatbot psychosis memetic algorithm turing 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openai defeats radio host s...
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