This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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(Code and Post written using AI assistance)
Language models often hallucinate because, at each step, they choose between several words that all look statistically reasonable but actually point in different directions meaning-wise. Standard sampling methods don’t notice this — they only care about which tokens are likely, not whether those tokens agree on what is being said. The Semantic-LLM-Interpreter changes this by looking at the meaning of the top candidate tokens, finding the “middle” meaning they mostly share, and favoring words that stay close to that shared intent. This makes the model less likely to jump onto a plausible-sounding but semantically off-track continuation.
From an alignment angle, this helps with a very practical problem: models sounding confident while being wrong. Instead of trying to fix hallucinations after the fact, this approach reduces them at generation time by nudging the model to stick to a stable line of thought. That leads to answers that are more consistent, less imaginative in the wrong places, and easier to trust. It doesn’t solve alignment on its own, but it points to a useful idea: shaping how models choose their words can be just as important as teaching them what to say.
The repository for this interpreter is open source, with more details of how it works in README.md: https://github.com/brodie-eaton/Semantic-LLM-Interpreter
(Code and Post written using AI assistance)
Language models often hallucinate because, at each step, they choose between several words that all look statistically reasonable but actually point in different directions meaning-wise. Standard sampling methods don’t notice this — they only care about which tokens are likely, not whether those tokens agree on what is being said. The Semantic-LLM-Interpreter changes this by looking at the meaning of the top candidate tokens, finding the “middle” meaning they mostly share, and favoring words that stay close to that shared intent. This makes the model less likely to jump onto a plausible-sounding but semantically off-track continuation.
From an alignment angle, this helps with a very practical problem: models sounding confident while being wrong. Instead of trying to fix hallucinations after the fact, this approach reduces them at generation time by nudging the model to stick to a stable line of thought. That leads to answers that are more consistent, less imaginative in the wrong places, and easier to trust. It doesn’t solve alignment on its own, but it points to a useful idea: shaping how models choose their words can be just as important as teaching them what to say.
The repository for this interpreter is open source, with more details of how it works in README.md: https://github.com/brodie-eaton/Semantic-LLM-Interpreter