5 ms·

> this time there exist a bunch of clear products Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I

> this time there exist a bunch of clear products Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples. > in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable. This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases. > today's stuff is useful now Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage? I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.

andreilys 2y ago
1. Content Generation: LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence. 2. Customer Service and Support: LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support. 3. Summarization and Insights: LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning. 4. HR Candidate Screening: Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles. 5. Legal Document Review: Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.
- theappsecguy 2y ago
  Dear lord, if someone started relying on LLMs for legal documents, their clients would be royally screwed…
  - KoolKat23 2y ago
    They're currently already relying on overworked, underpaid interns who draft those documents. The lawyer is checking it anyway. Now the lawyer and his intern have time to check it.
    - ace32229 2y ago
      I have no idea what type of law you're talking about here, but (given the context of the thread) I can guarantee you major firms working on M&As are most definitely not using underpaid interns to draft those documents. They are overpaid qualified solicitors.
      - KoolKat23 2y ago
        Apologies I mean candidate attorneys when I say interns. Those overpaid qualified attorneys, read it and sign off on it.
    - District5524 2y ago
      I suggest we do not repeat the myth and urban legend that LLMs are good for legal document review. I had a couple of real use cases used for real clients who were hyped about LLMs to be used for document review and trying to save salary, for Engish language documents. We've found Kira, Luminance and similar due diligence project management stuff as useful being a timesaver if done right. But not LLMs. Due to longer context windows, it is possible to ask LLMs the usual hazy questions that people ask in a due diligence review (many of which can be answered dozens of different ways by human lawyers). Is there a most favoured nation provision in the contract, is there a financial cap limiting the liability of the seller or the buyer, governing law etc. Considering risks of uploading such documents into ChatGPT, you are stuck with Copilot M365 etc. or some outrageously expensive "legal specific" LLMs that I cannot test. Just to be curious with Copilot I've asked five rather simple questions for three different agreements (where we had the golden answer), and the results were quite unequal, but mostly useless - in one contract, it incorrectly reported for all questions that these cannot be answered based on the contract (while the answers were clearly included in the document), in an another, two questions were answered correctly, two questions not answered precisely (just governing law being US instead of the correct answer being Michigan, even after reprompting to give the state level answer, not "USA") and hallucinated one answer incorrectly. In the third one, three answeres were hallucinated incorrectly, answered one correctly and one provision was not found. Of course, it's better to have a LEGAL specific benchmark for this, but 75% hallucination in complex questions is not something that helps your workflow (https://hai.stanford.edu/news/hallucinating-law-legal-mistakes-large-language-models-are-pervasive https://hai.stanford.edu/news/hallucinating-law-legal-mistak...) I don't recommend at least LLMs to anyone for legal document reviews, even for the English language.
Nullabillity 2y ago
Calling LLMs autocompleters is an insult to autocompleters.
paretoer 2y ago
IMO the unreasonable uselessness of LLMs is because for most tasks involving language the accuracy needs to be unbelievably high to have any real value at all. We just don't have that. We have autocomplete on steroids and many people are fooling themselves that if you just take more steroids you will get better and better results. The metaphor is perfect because if you take more and more steroids you get less and less results. It is why in reality we have had almost no progress since April 2023 and chatGPT 4.

andreilys 2y ago

1. Content Generation: LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence. 2. Customer Service and Support: LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support. 3. Summarization and Insights: LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning. 4. HR Candidate Screening: Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles. 5. Legal Document Review: Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.

theappsecguy 2y ago
Dear lord, if someone started relying on LLMs for legal documents, their clients would be royally screwed…
- KoolKat23 2y ago
  They're currently already relying on overworked, underpaid interns who draft those documents. The lawyer is checking it anyway. Now the lawyer and his intern have time to check it.
  - ace32229 2y ago
    I have no idea what type of law you're talking about here, but (given the context of the thread) I can guarantee you major firms working on M&As are most definitely not using underpaid interns to draft those documents. They are overpaid qualified solicitors.
    - KoolKat23 2y ago
      Apologies I mean candidate attorneys when I say interns. Those overpaid qualified attorneys, read it and sign off on it.
  - District5524 2y ago
    I suggest we do not repeat the myth and urban legend that LLMs are good for legal document review. I had a couple of real use cases used for real clients who were hyped about LLMs to be used for document review and trying to save salary, for Engish language documents. We've found Kira, Luminance and similar due diligence project management stuff as useful being a timesaver if done right. But not LLMs. Due to longer context windows, it is possible to ask LLMs the usual hazy questions that people ask in a due diligence review (many of which can be answered dozens of different ways by human lawyers). Is there a most favoured nation provision in the contract, is there a financial cap limiting the liability of the seller or the buyer, governing law etc. Considering risks of uploading such documents into ChatGPT, you are stuck with Copilot M365 etc. or some outrageously expensive "legal specific" LLMs that I cannot test. Just to be curious with Copilot I've asked five rather simple questions for three different agreements (where we had the golden answer), and the results were quite unequal, but mostly useless - in one contract, it incorrectly reported for all questions that these cannot be answered based on the contract (while the answers were clearly included in the document), in an another, two questions were answered correctly, two questions not answered precisely (just governing law being US instead of the correct answer being Michigan, even after reprompting to give the state level answer, not "USA") and hallucinated one answer incorrectly. In the third one, three answeres were hallucinated incorrectly, answered one correctly and one provision was not found. Of course, it's better to have a LEGAL specific benchmark for this, but 75% hallucination in complex questions is not something that helps your workflow (https://hai.stanford.edu/news/hallucinating-law-legal-mistakes-large-language-models-are-pervasive https://hai.stanford.edu/news/hallucinating-law-legal-mistak...) I don't recommend at least LLMs to anyone for legal document reviews, even for the English language.

Nullabillity 2y ago

Calling LLMs autocompleters is an insult to autocompleters.

paretoer 2y ago

IMO the unreasonable uselessness of LLMs is because for most tasks involving language the accuracy needs to be unbelievably high to have any real value at all. We just don't have that. We have autocomplete on steroids and many people are fooling themselves that if you just take more steroids you will get better and better results. The metaphor is perfect because if you take more and more steroids you get less and less results. It is why in reality we have had almost no progress since April 2023 and chatGPT 4.