To this question “How and when should Large Language Models be developed and deployed to reap their benefits without their harmful consequences?”
Here is the link to the detailed article who’s solutions I am providing in this article:
Large Language Models (LLMs) takes as input textual data in a Natural Language formats. The Large Language Models takes huge dumps form internet to learn useful things. And often end up learning some wrong things and then come in focus for bad things learned.
There are two things—–
- Children getting wrong contents,
- Adults getting wrong contents, this is primarily due to fake news and fake accounts fed into the AI system. We shall focus on this in next article.
This article focus on how to make AI languages safe for children and teenagers.
How does a small child learn wrong words? From wrong things he/she gets to hear. In the same way the system you are blaming is not being taught well.
All one need to change is the content being taught not the teaching mythology. And this does not require lot of new algorithms. This needs a filtering system. A filtering system filters the input in form of sentences for apt inputs to be fed to the child–here the child is the AI language model–learning with the inputs given to it.
Just like spam mails are separated from normal mails to secure us from harmful emails. In the same way, these harmful contents can be seperated from AI language model. Which can be seen as a child learning things.
Regarding mentions of harmful contents for children. I suggest the filtering system which filters the Natural Language Inputs to the AI based Deep Neural Models to make three different models. They are given as follows:
- Model for children
- Model for teenagers
- Model for adults
The model for children and teenagers are fed in filtered input — i.e. the algorithm to non-adults will filter out all contents not meant for children.
While adults do have right to know what is happening in world, and their model may not need so much of filtering. Just fake news need to be separated from such systems.
What we feed to the network is what we it will learn. These are basic pre-processing NLP task in LLMs in this process.
This will solve many problems. Sentence level filtering need to be done here as many times a document is very useful but just few sentences are not meant for children.
Now–AI can help in filtering the sentences which are not apt for children and teenagers by learning using semi-supervised techniques. Or fully unsupervised way as well using sentiment of sentence as a classifying target class.
However, the first basic model can be up with keywords having negative sentiments. Sentiment analysis is a well developed technique of Natural Language Processing and this can be through sentiment lexicons or sentiment models learned for some processes. Once sentiment of a sentence is computed as negative — one may delete this sentence from the training data fed to the AI-child in learning mode to make a model for children. Similar model for teenagers can be made where somewhat more flexibility can be provided to sentiment scores. But for children < 12 — only positive sentiment scores need to be put on the LLM AI model generation. For adults what needs to be filtered is fake accounts through which fake news is spread–not these words–but fake news——needs to be blocked from training — this needs to be learned.
Regarding gender bias and similar issues these are post processing NLP task which needs to be done after training LLMs. Not much from algorithm need to be changed for it, though algorithms can grow more for serving better. Some logic need to be added in this — post-processing Natural Language Logic Rules Checks– Some hard work is always needed in Language tasks as against image processing things.
Some people call it statistical parroting system. Now do we call children learning new concepts as mugging things ? No– They are learning—-Well then see from where we came in computing – The basic mode of communication used to be – ON signal and OFF signal as in an bitwise communication technology. From there we came to now broad options that can be well used with natural language based instructions doing all work which used to be typed in in form of 0’s and 1’s once on mainframes. Hence what some people call statistical mugging system can be seen as an intermediate phase in lifecycle of development of a more AGI intelligent natural language system. Work is needed to make the systems more intelligent.
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