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That's not my experience with the leading models, & I'm seeing others observe even when LLMs are wrong, they often supply interesting potential avenues to consi
by gojomo 3y ago
That's not my experience with the leading models, & I'm seeing others observe even when LLMs are wrong, they often supply interesting potential avenues to consider (especially in the coding-assistant/debugging domains).
Also, techniques for tamping-down hallucinations are improving rapidly, with teams finding...
• ways to detect likely hallucinations from patterns in internal activations
• extra conditioning to reduce hallucinations
• success using explicit requests that the LLM lies to train effective classifiers for detecting other unrequested falsehoods
• ways to check output against various ground-truths to detect & correct errors
To focus on the real-but-shrinking number of failure modes will miss the almost unbounded upside from continuous improvements.
- Retric 3y agoReducing hallucinations is useful but doesn’t imply that the remaining hallucinations are going to be valuable. In terms of coding the costs of an LLM using some API function that doesn’t exist isn’t that high because we have tools to detect such nonsense and can look at the API in question. In science people don’t get to read realities underlying API’s.