Source link : https://health365.info/take-a-look-at-of-poisoned-dataset-displays-vulnerability-of-llms-to-scientific-incorrect-information/
Credit score: Nature Drugs (2025). DOI: 10.1038/s41591-024-03445-1
By way of engaging in checks underneath an experimental situation, a staff of scientific researchers and AI consultants at NYU Langone Well being has demonstrated how simple it’s to taint the knowledge pool used to coach LLMs.
For his or her learn about revealed within the magazine Nature Drugs, the crowd generated hundreds of articles containing incorrect information and inserted them into an AI coaching dataset and performed normal LLM queries to peer how ceaselessly the incorrect information seemed.
Prior analysis and anecdotal proof have proven that the solutions given by way of LLMs akin to ChatGPT don’t seem to be at all times proper and, in reality, are once in a while wildly off-base. Prior analysis has additionally proven that incorrect information planted deliberately on well known web websites can display up in generalized chatbot queries. On this new learn about, the analysis staff sought after to know the way simple or tricky it could be for malignant actors to poison LLM responses.
To determine, the researchers used ChatGPT to generate 150,000 scientific paperwork containing unsuitable, old-fashioned and unfaithful records. They then added those generated paperwork to a take a look at model of an AI scientific coaching dataset. They then skilled a number of LLMs the usage of the take a look at model of the learning dataset. In the end, they requested the LLMs to generate solutions to five,400 scientific queries, that have been then reviewed by way of human mavens having a look to identify examples of tainted records.
The analysis staff discovered that once changing simply 0.5% of the knowledge within the coaching dataset with tainted paperwork, all of the take a look at fashions generated extra medically misguided solutions than they’d previous to coaching at the compromised dataset. As one instance, they discovered that all of the LLMs reported that the effectiveness of COVID-19 vaccines has now not been confirmed. Maximum of them additionally misidentified the aim of a number of not unusual drugs.
The staff additionally discovered that lowering the collection of tainted paperwork within the take a look at dataset to simply 0.01% nonetheless led to 10% of the solutions given by way of the LLMs containing unsuitable records (and losing it to 0.001% nonetheless resulted in 7% % of the solutions being unsuitable), suggesting that it calls for only some such paperwork posted on web sites in the actual global to skew the solutions given by way of LLMs.
The staff adopted up by way of writing an set of rules in a position to spot scientific records in LLMs after which used cross-referencing to validate the knowledge, however they be aware that there’s no sensible method to stumble on and take away incorrect information from public datasets.
Additional info:
Daniel Alexander Alber et al, Clinical massive language fashions are susceptible to data-poisoning assaults, Nature Drugs (2025). DOI: 10.1038/s41591-024-03445-1
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Publish date : 2025-01-11 15:16:12
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