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The achilles heels of current LLMs are: 1. Hallucinations 2. Prompt injections Currently, there is no known way to detect either using LLMs themselves. As a
by lnkdinsuxs 2y ago
The achilles heels of current LLMs are:
1. Hallucinations
2. Prompt injections
Currently, there is no known way to detect either using LLMs themselves. As a research assistant, if the LLM hallucinates, and it always sounds extremely confident when it does so, the LLM itself is of little use and causes additional burden, defeating the whole point of this.
Maybe an external validation step that employs a pagerank like algorithm is needed to detect and flag hallucinations? If so, how valuable would that company be?
- anon373839 2y agoI’d add: 3. Steering them The way prompting devolves into trial and error with little rhyme or reason can make it pretty frustrating to slot them into automation pipelines. It’s hard to foresee all the ways they can go off the rails or give strangely inconsistent results.