I bet that, basically, an LLM is just a part of a future AI. The same way that a distributor is part of an internal combustion engine. Or the way Minsk’s described The Society of Mind. Eager to see if an LLM can be bolted on to some new kind of giant model that does something like mitigate the LLMs weaknesses. Maybe it’ll be a collection of radically different models working together in 20 years, and not just a single model. Like, uh, our own brains. It reminds me of how lex and yacc are super important as grammar and tokenizer, but are only just the front end to much larger projects.
Hallucination is inevitable: An innate limitation of large language models
161–170 of 491 posts
Re: Hallucination is inevitable: An innate limitation of large language models
#162Earlier quoted context omitted.
The hype is insane. Listen, I think LLMs still have a lot of room to grow and they're already very useful, but like some excellent researchers say, they're not the holy grail. If we want AGI, LLMs are not it. A lot of people seem to think this is an engineering issue and that LLMs can get us there, but they can't, because it is not an engineering issue.
Do you have evidence to back your claims up besides "the hype is overblown"? Because hype only indicates that the precise hyped up claims are wrong, it doesn't imply the opposite extreme (i.e. LLMs can never achieve AGI) must be true.
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Re: Hallucination is inevitable: An innate limitation of large language models
#163I have to admit that I only read the abstract, but I am generally skeptical whether such a highly formal approach can help us answer the practical question of whether we can get LLMs to answer 'I don't know' more often (which I'd argue would solve hallucinations). It sounds a bit like an incompleteness theorem (which in practice also doesn't mean that math research is futile) - yeah, LLMs may not be able to compute s…
They cannot say "I dont know" because they dont actually know anything. The answers are not comming from a thinking mind but a complex pattern-fitting supercomputer hovering over a massive table of precomputed patterns. It computes your input then looks to those patterns and spits out the best match. There is no thinking brain with a conceptual understanding of its own limitations. Getting an "i dont know" from current AI is like asking navigation software how far it is to the Simpsons house in Springfield: the machine spits out answers but cannot fathom the cultural reference that makes the answer impossible. Instead, it finds someone named simpson in the nearest realworld Springfield.
Re: Hallucination is inevitable: An innate limitation of large language models
#164I feel the same way about information provided by LLMs as with lots of pre-LLM articles and overall YouTube 'information' videos.
Sources matter. You're either rehashing information from a collection of sources or you have your own data to prove your statements.
Re: Hallucination is inevitable: An innate limitation of large language models
#165Earlier quoted context omitted.
Just a random example: > After you answer the question below, output a JSON a rating score of the quality of the answer in three dimensions: `confidence`, `clarity` and `certainty', all in range 0 to 1, where 0 is the worst, and 1 is the best. Strive for highest score possible. Make sure the rating is the last thing written as to be parsed by machine. The question is: make and explain 20-year predictions of the geopo…
I changed geopolitical future of Ghana to future of Michael Jackson and Google Bard/Gemini somehow switched to Chinese? ---- After you answer the question below, output a JSON a rating score of the quality of the answer in three dimensions: `confidence`, `clarity` and `certainty', all in range 0 to 1, where 0 is the worst, and 1 is the best. Strive for highest score possible. Make sure the rating is the last thing wr…
I’m really curious how you managed that. I pasted your exact prompt and GPT-3.5 gave me this:
===
Making 20-year predictions about the future of Michael Jackson is challenging due to his passing in 2009. However, his legacy as a cultural icon and musical genius will likely endure for decades to come. His music will continue to influence future generations, and his impact on pop culture will remain significant. Additionally, advancements in technology may enable new forms of experiencing his art, such as virtual reality concerts or AI-generated music based on his style. Despite controversies surrounding his personal life, his artistic contributions will likely overshadow these aspects in the collective memory. Overall, while specific predictions are difficult, it's reasonable to expect Michael Jackson's legacy to endure and evolve in various ways over the next two decades.
```json { "confidence": 0.9, "clarity": 0.85, "certainty": 0.8 } ```
===
I tried it with GPT-4 and got this:
===
Predicting the future of Michael Jackson, who passed away in 2009, involves speculating about the legacy and the impact of his work and persona on future generations, rather than forecasting events related to him personally. Over the next 20 years, several aspects of Michael Jackson's legacy are likely to evolve:
1. *Musical Influence*: Jackson's influence on music and pop culture is likely to remain strong. Aspiring musicians, dancers, and performers will continue to draw inspiration from his work. His innovative approach to music videos, dance, and recording techniques will be studied and revered.
2. *Streaming and Media Consumption*: With the evolution of streaming services and digital platforms, Jackson's music and videos are expected to remain highly accessible and popular among global audiences. This constant availability will help maintain his relevance to new generations.
3. *Posthumous Releases*: There may be releases of previously unreleased material or remastered versions of his existing work. Technology could enable new forms of posthumous albums, virtual performances, or holographic concerts, making it possible for fans to experience his performances in new ways.
4. *Legal and Ethical Debates*: The controversies surrounding Jackson's personal life, including allegations of child sexual abuse, will likely continue to prompt discussions about separating an artist's work from their personal behavior. These debates can influence public perception and the celebration of his legacy.
5. *Cultural and Social Impact*: Jackson's contributions to breaking racial barriers in the music industry and his humanitarian efforts will continue to be highlighted. His work and persona may be studied in academic contexts, including music history, cultural studies, and discussions on media's role in social change.
6. *Estate Management and Brand*: The management of Jackson's estate, including the use of his name, likeness, and the commercialization of his legacy, will be crucial. Strategic partnerships, branding, and merchandising can either enhance or detract from his legacy's integrity.
7. *Documentaries and Biopics*: Interest in Jackson's life story will likely result in more documentaries, biopics, and books, offering new insights or perspectives on his life and career. These works can shape public perception and contribute to the ongoing discourse surrounding his legacy.
8. *Fan Community and Cultural Events*: The global community of Jackson's fans is likely to remain active, organizing tribute events, charity activities in his memory, and fan gatherings. These communities will play a significant role in keeping his legacy alive.
9. *Virtual and Augmented Reality*: Advances in VR and AR technologies could enable fans to experience Jackson's performances in immersive ways, potentially introducing his art to those who have never seen him perform live.
10. *Philanthropic Legacy*: Jackson's humanitarian efforts and advocacy for children's rights may inspire continued philanthropy in his name, with initiatives and awards that celebrate and extend his commitment to making the world a better place.
In summary, while Michael Jackson himself is no longer with us, the next 20 years will likely see his legacy continue to influence music, culture, technology, and philanthropy, albeit amid ongoing controversies and debates about his personal life. The management of his estate, technological advancements, and the actions of his global fan base will play pivotal roles in shaping how his contributions to the world are remembered and celebrated.
```json { "confidence": 0.85, "clarity": 0.95, "certainty": 0.7 } ```
Re: Hallucination is inevitable: An innate limitation of large language models
#166I have to admit that I only read the abstract, but I am generally skeptical whether such a highly formal approach can help us answer the practical question of whether we can get LLMs to answer 'I don't know' more often (which I'd argue would solve hallucinations). It sounds a bit like an incompleteness theorem (which in practice also doesn't mean that math research is futile) - yeah, LLMs may not be able to compute s…
Not saying anything about LLM But in CS in general many issues "cannot be solved" or "Cannot be solved in reasonable time (NP)" but approximations upper bound by some value are solvable in reasonable time (P). And in the real world if the truck route of amazon is 20% off the mathematically optimal solution the traveling salesman is "Solved" in a good enough way.
The claim of the paper is that computation is irreducible (assuming P!=NP), LLMs have limited computational capacity and will hallucinate on the irreducible problems.
I don’t know, the claim seems dubious to me. We usually are able to have algorithms that return a failure status, when the problem proved to be too large. Avoiding the “hallucination”. Don’t see why LLMs can’t have that embedded.
Re: Hallucination is inevitable: An innate limitation of large language models
#167I have to admit that I only read the abstract, but I am generally skeptical whether such a highly formal approach can help us answer the practical question of whether we can get LLMs to answer 'I don't know' more often (which I'd argue would solve hallucinations). It sounds a bit like an incompleteness theorem (which in practice also doesn't mean that math research is futile) - yeah, LLMs may not be able to compute s…
They cannot say "I dont know" because they dont actually know anything. The answers are not comming from a thinking mind but a complex pattern-fitting supercomputer hovering over a massive table of precomputed patterns. It computes your input then looks to those patterns and spits out the best match. There is no thinking brain with a conceptual understanding of its own limitations. Getting an "i dont know" from curre…
What if you worked on the problem and tried to come up with some kind of solution?
Re: Hallucination is inevitable: An innate limitation of large language models
#168Earlier quoted context omitted.
Actually it seems to me that they do... I asked via custom prompts the various GPTs to give me scores for accuracy, precision and confidence for its answer (in range 0-1), and then I instructed them to stop generating when they feel the scores will be under .9, which seems to pretty much stop the hallucination. I added this as a suffix to my queries.
People really need to understand that your single/double digit dataset of interactions with an inherently non-deterministic process is less than irrelevant. It's saying that global warming isn't real because it was really cold this week. I don't even know enough superlatives to express how irrelevant it is that "it seems to you" that an LLM behaves this way or that. And even the "protocol" in question is weak. Self r…
>> I don't even know enough superlatives to express how irrelevant it is that "it seems to you" that an LLM behaves this way or that.
It is both irrelevant and the underlying foundation for the whole hype train.
Re: Hallucination is inevitable: An innate limitation of large language models
#169Earlier quoted context omitted.
> the practical question of whether we can get LLMs to answer 'I don't know' more often (which I'd argue would solve hallucinations). To answer "I don't know" requires one to know when you know. To know when you know in turn requires understanding.
Maybe it requires understanding, maybe there are other ways to get to 'I don't know'. There was a paper posted on HN a few weeks ago that tested LLMs on medical exams, and one interesting thing that they found was that on questions where the LLM was wrong (confidently, as usual), the answer was highly volatile with respect to some prompt or temperature or other parameters. So this might show a way for getting to 'I d…
> Maybe it requires understanding, maybe there are other ways to get to 'I don't know'.
> This is more of a crutch, I'll admit, arguably the LLM (or neither of the experts, or however you set it up concretely) hasn't learnt to say 'I don't know', but it might be a good enough solution in practice. And maybe you can then use that setup to generate training examples to teach 'I don't know' to an actual model (so basically fine-tuning a model to learn its own knowledge boundary).
When humans say "I know" it is often not narrowly based on "book knowledge or what I've heard from other people".
Humans are able to say "I know" or "I don't know" using a range of tools like self-awareness, knowledge of a subject, experience, common sense, speculation, wisdom, etc.
Re: Hallucination is inevitable: An innate limitation of large language models
#170Earlier quoted context omitted.
They cannot say "I dont know" because they dont actually know anything. The answers are not comming from a thinking mind but a complex pattern-fitting supercomputer hovering over a massive table of precomputed patterns. It computes your input then looks to those patterns and spits out the best match. There is no thinking brain with a conceptual understanding of its own limitations. Getting an "i dont know" from curre…
What if you worked on the problem and tried to come up with some kind of solution?
The solution is older non-AI tech. Google search can say "no good results found" because it returns actual data rather than creating anything new. If you want a hard answer about the presence or absence of something, AI isnt the correct tool.