Is this just using LLM to be cool? How does pure LLM with basic "In the scale between 0-10 ..." prompt stack up against traditional, battle-tested sentiment analysis tools?
Gemini suggests NLTK and spaCy
Gemini suggests NLTK and spaCy
Also, many exisiting sentiment analysis tools have a lot of research behind them that can be referenced when interpreting the results (known confounds etc). I don't think there is yet an equivalent for the LLM approach
For example, EVs. Compare EVs to ICEVs and you can point out a lot of faults, but ICEVs have had 100 years of refinement. Perhaps you're comparing battle hardened SA with fledgling LLM based SA?
Never don't do new things, not only when its for fun, but especially when its for fun. If you want to be closed minded, that's your choice, but don't try to put that mentality onto others.
Keep your non-hacker mindset to yourself.
LLMs really are almost magic in how they can help in this space; and setting them up is often just getting an API key and throwing some money and webservice calls at them.
Setting up spaCy is just `pip install spacy`. No need to worry about GPUs or dedicated services like you do with LLMs.
Its like the linux of operating systems. Sure you can handwrite up some custom OS more specialized for a purpose. But its much easier to just use linux, which everyone understands on a basic level and is extremely robust, and modifying it slightly for the end goal.
And saying "Traditional sentiment analysis" tools are "Battle tested" is laughable. LLMs in the past year alone, probably has 1000x the cumulative usage of all sentiment analysis tools in history.
LLMs get 100 billion + each year in research, improvements, engineering, optimisations.
LLMs keep rapidly improving year to year in capabilities. Sonnet 3.5 already obliterates the original GPT-4 in every aspect.
LLMs keep getting cheaper year to year. Gemini flash is like 100x cheaper than the original GPT3.5.
You can onboard any person who can write python, to start using LLMs to perform language analysis in a day. Versus weeks to use these traditional tools.
Nearly all NLP tasks will be standardised to use LLMs as the baseline default tool. Sure there'll be some short term degradations in some specific aspect, but there's no stopping the tide.
By the way, traditional ML-based translation is also pretty much dead and replaced by LLMs. I've been seeing an explosion in fan-translations done by say Sonnet 3.5, the improvement in fluency and accuracy is just radical and extreme, I often don't even notice the AI-translation anymore.
You can use similar datasets and the latest model architectures and if you train a model purely for sentiment analysis it will be better than frontier general purpose LLMs for sentiment analysis.
Its not comparing C against Javascript, its comparing Ada against Javascript. Ada is not going to be any faster than javascript because its too niche and therefore underoptimised.
The theoretical minimum computation required by LLMs is far higher than traditional simple NLP algorithms. But the practical computation cost of LLMs will soon be cheaper, because LLMs get so much investment and use, there's massive full-stack optimizations all the way from the GPU to the end libraries.