How Will the LLM Hallucination Problem Be Solved?

18

Ṁ4622029

1D

1W

1M

ALL

7%

12%

Vector Embeddings (as with Pinecone https://www.pinecone.io/)

2%

Filtering (as with Deepmind AlphaCode https://www.deepmind.com/blog/competitive-programming-with-alphacode)

0.3%

Ensemble Combined with Fine Tuning

1.1%

Joint Embedding Predictive Architecture (https://arxiv.org/pdf/2301.08243.pdf)

0.6%

Feed Forward Algorithms (https://www.cs.toronto.edu/~hinton/FFA13.pdf)

23%

Bigger model trained on more data + RL

3%

Vigger models + prompt engineering

50%

It won't be

0.3%

Giving all LLMs access to the internet and databases of scientific papers

By the year 2028, how will the Hallucination Problem have been solved for the vast majority of applications out there?

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@LukeFrymire this would imply that either the options in my market are overvalued or your market is overvalued.

@PatrickDelaney I think the resolution criteria is fairly different. Mine requires that a scale-based solution is possible, yours requires it to be the primary method in production.

@VictorLevoso ught hit v instead of b in keyboard and didn't look at the question properly before clicking submit and now can't edit it or erase it.

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