This is a bad idea. It’s extremely likely to hallucinate at one point or another no matter how many tools you equip it with, and humans will eventually miss some fully made up citation or completely misrepresented conclusion.
I’m a professional software engineer and I’ve used RAG. It doesn’t prevent all hallucinations. Nothing can. The “hallucinations” are a fundamental part of the LLM architecture.
I use LLMs daily as a professional software engineer. I didn’t downvote you and I’m not disengaging my thinking here. RAGs don’t solve everything, and it’s better not to sacrifice scientific credibility to the altar of convenience.
It’s always been easier to lie quickly than to dig for the truth. AIs are not consistent, regardless of the additional appendages you give them. They have no internal consistency by their very nature.
What would the failure rate on this be? What would the rate have to be to actually matter? Literally it would just poll the abstract and spit out yes no undecided. That is in the abstract. There is very little chance of there being any hallucinations that are meaningful at a degree large enough to vary literally anything.
Have you never had it organize things or analyze sentiments? I understand if that’s not your use case but this is pretty fundamentally an easy application of AI.
And this isn’t even really a great application for RAG. Papermaps just goes off of references and citations. Perhaps a sentiment analysis would be marginally useful, but since you need a human to verify all LLM outputs it would be a dubious time savings.
The system scores review papers very favorably and the “yes/no/maybe” conclusion is right in the abstract, usually the last sentence or two of it. This is not a prime candidate for any LLM, it’s simple database operations on srtuctured data that already exists. There’s no use case here.
Perhaps a sentiment analysis would be marginally useful, but since you need a human to verify all LLM outputs it would be a dubious time savings.
Thank you, yes. That’s exactly my point. You’d need a human to verify all of the outputs anyways, and these are literally machines that exclusively make text that humans find believable, so you’re likely adding to the problem of humans messing stuff up moreso than speeding anything up. Being wrong fast has always been easy, so it’s no help here.
This is a bad idea. It’s extremely likely to hallucinate at one point or another no matter how many tools you equip it with, and humans will eventually miss some fully made up citation or completely misrepresented conclusion.
Google RAG
There are tons of AIs that are not auto regressive LLMs
I’m a professional software engineer and I’ve used RAG. It doesn’t prevent all hallucinations. Nothing can. The “hallucinations” are a fundamental part of the LLM architecture.
Are the down votes because people genuinely think this is an incorrect answer, or because they dislike anything remotely pro-AI?
Both probably. Thought terminating cliches and all that. The most useful tool maybe ever. Wild.
I use LLMs daily as a professional software engineer. I didn’t downvote you and I’m not disengaging my thinking here. RAGs don’t solve everything, and it’s better not to sacrifice scientific credibility to the altar of convenience.
It’s always been easier to lie quickly than to dig for the truth. AIs are not consistent, regardless of the additional appendages you give them. They have no internal consistency by their very nature.
What would the failure rate on this be? What would the rate have to be to actually matter? Literally it would just poll the abstract and spit out yes no undecided. That is in the abstract. There is very little chance of there being any hallucinations that are meaningful at a degree large enough to vary literally anything.
Have you never had it organize things or analyze sentiments? I understand if that’s not your use case but this is pretty fundamentally an easy application of AI.
And this isn’t even really a great application for RAG. Papermaps just goes off of references and citations. Perhaps a sentiment analysis would be marginally useful, but since you need a human to verify all LLM outputs it would be a dubious time savings.
The system scores review papers very favorably and the “yes/no/maybe” conclusion is right in the abstract, usually the last sentence or two of it. This is not a prime candidate for any LLM, it’s simple database operations on srtuctured data that already exists. There’s no use case here.
Thank you, yes. That’s exactly my point. You’d need a human to verify all of the outputs anyways, and these are literally machines that exclusively make text that humans find believable, so you’re likely adding to the problem of humans messing stuff up moreso than speeding anything up. Being wrong fast has always been easy, so it’s no help here.