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Set up to use, sure. Learn? Learning isn’t deployment in this context. Setting up spaCy is just `pip install spacy`. No need to worry about GPUs or dedicated services like you do with LLMs.
yeah, then you have to learn the API. Basically every option for running LLMs have converged on the openAI (web) API.
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What do you mean? Deploying something like spaCy is far easier than deploying an LLM in my experience.
pip install vllm, boom you have an openapi-compatible webserver. No further action necessary.
I don't know about this analysis and its conclusions. I'll just use this as a jumping point to selfishly spout my own human observations. For context, I'm someone who uses HN to search for topics I'm interested in, rather than something like Google or Reddit. - For anything SF community-related, most hits are from 10+ years ago. Lots of "hey we have a space in soma, any local startups want to hang and drink beers?" o…
TFA’s sentiment decrease tracks very closely with the huge uptick in user creation that started in 2022. HN isn’t really a tech site anymore, it’s about vibes. That makes sense given that in 2024 there’s a million places online talking about tech so HN only has its culture to distinguish itself. This wasn’t the case in 2008. The vibes here, along with the older demographics of the site, are increasingly nostalgic and cynical.
It'll all probably go the same way as Slashdot did which went through the same cycle (replace "VC huckster" with "Microsoft" and "surveillance capitalism" with "three letter agencies") until it too gets replaced by a site/community with energetic younger users creating new things.
I did a similar kind of process for my own chat logs. I have about 11M tokens worth of logs, and it took 2 days to crunch all of them with ollama and LLaMA 3.1 8B on my MacBook. It's slow, but free. I generated title, summary, keywords and hierarchical topics up to 3 levels up from the original text. My plan for now is to put them in a vector search engine, which, incidentally, was made with Sonnet 3.5 with very litt…
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Sorry, but not really. If you know what you do, you don't just pick an LLM. LLMs are trained/built for a specific task: text generation. Other models are trained on different tasks. If you know what you do, you compare models (I don't mean LLM models with that!) and choose the best performing. Just because LLMs receive more training doesn't mean they have a better performance. Very weird and flawed way of thinking. T…
I have to agree with the parent. LLMs are excellent at a large range of NLP tasks. Of course they are not going to replace all ML models, but when it comes to NLP they are clearly better than lots of trained models (e.g. https://arxiv.org/pdf/2310.18025 ).
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.
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 https://www.nltk.org/ https://spacy.io/
I'm wondering how their LLM parsing 250 mil words in 9 hours compares with performance of traditional sentiment analysis. 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
> NFL (915 posts) > Football (206 posts) Either hacker news really likes the national forensic league, or these LLM-categories are a bit dubious. Also hmmm: > American football (7 posts) > American_football (6 posts)
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I have to agree with the parent. LLMs are excellent at a large range of NLP tasks. Of course they are not going to replace all ML models, but when it comes to NLP they are clearly better than lots of trained models (e.g. https://arxiv.org/pdf/2310.18025 ).
LLMs are general purpose tools and absolutely are not better than trained models (using the latest techniques) for a specific task. I mean, that's obviously true if you think about it. 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.
I wonder if the dip is more about LLama3 70b training and data than a change in sentiment. The data cut-off was Dec 2023 for 70b. That looks to coincide with the reversal of the dip.
That's an interesting hypothesis but the words we use to express agreement and disagreement haven't changed much. We don't try to retrieve articles/topics from the model, which would be affected by the cutoff, just asking it to analyze the sentiment or summarize the content provided in a prompt