Somewhat unrelated to the main point of your post, but; How close are you to solving the wanting-to-look-good problem?
I run a startup in a completely different industry, and we’ve invested significant resources in trying to get an LLM to interact with a customer, explain and make dynamic recommendations based on their preferences. This is a more high-touch business, so traditionally this was done by a human operator. The major problem we’ve encountered is that it’s almost impossible to have an LLM to admit ignorance when it doesn’t have the information. It’s not outright hallucinating, so much as deliberately misinterpreting instructions so it can give us a substantial answer, whether or not one is warranted.
We’ve put a lot of resources in this, and it’s reached the point where I’m thinking of winding down the entire project. I’m of the opinion that it’s not possible with current models, and I don’t want to gamble any more resources on a new model that solves the problem for us. AI was never our core competency, and what we do in a more traditional space definitely works, so it’s not like we’d be pivoting to a completely untested idea like most LLM-wrapper startups would have to do.
I thought I’d ask here, since if the problem is definitely solvable for you with current models, I know it’s a problem with our approach and/or team. Right now we might be banging our heads against a wall, hoping it will fall, when it’s really the cliffside of a mountain range a hundred kilometers thick.
Maybe we are talking about different problems, but we found instructing models to give up (literally “give up”, I just checked the source) under certain conditions to be effective.
Somewhat unrelated to the main point of your post, but; How close are you to solving the wanting-to-look-good problem?
I run a startup in a completely different industry, and we’ve invested significant resources in trying to get an LLM to interact with a customer, explain and make dynamic recommendations based on their preferences. This is a more high-touch business, so traditionally this was done by a human operator. The major problem we’ve encountered is that it’s almost impossible to have an LLM to admit ignorance when it doesn’t have the information. It’s not outright hallucinating, so much as deliberately misinterpreting instructions so it can give us a substantial answer, whether or not one is warranted.
We’ve put a lot of resources in this, and it’s reached the point where I’m thinking of winding down the entire project. I’m of the opinion that it’s not possible with current models, and I don’t want to gamble any more resources on a new model that solves the problem for us. AI was never our core competency, and what we do in a more traditional space definitely works, so it’s not like we’d be pivoting to a completely untested idea like most LLM-wrapper startups would have to do.
I thought I’d ask here, since if the problem is definitely solvable for you with current models, I know it’s a problem with our approach and/or team. Right now we might be banging our heads against a wall, hoping it will fall, when it’s really the cliffside of a mountain range a hundred kilometers thick.
Maybe we are talking about different problems, but we found instructing models to give up (literally “give up”, I just checked the source) under certain conditions to be effective.