blog.sicuranext.com/influencing-llm-output-using-logprobs-and-token-distribution
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Influencing LLM Output using logprobs and Token Distribution
What if you could influence an LLM's output not by breaking its rules, but by bending its probabilities? In this deep-dive, we explore how small changes in user input (down to a single token) can shift the balance between “true” and “false”, triggering radically different completions.
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Influencing LLM Output using logprobs and Token Distribution
What if you could influence an LLM's output not by breaking its rules, but by bending its probabilities? In this deep-dive, we explore how small changes in user input (down to a single token) can shift the balance between “true” and “false”, triggering radically different completions.
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https://blog.sicuranext.com/influencing-llm-output-using-logprobs-and-token-distributionInfluencing LLM Output using logprobs and Token Distribution
What if you could influence an LLM's output not by breaking its rules, but by bending its probabilities? In this deep-dive, we explore how small changes in user input (down to a single token) can shift the balance between “true” and “false”, triggering radically different completions.
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22- titleInfluencing LLM Output using logprobs and Token Distribution
- titleTDD forward – interactive log‑prob viewer
- titleTDD forward – interactive log‑prob viewer (instance 2)
- titleTDD forward – interactive log‑prob viewer (instance 3)
- titleTDD forward – interactive log‑prob viewer (instance 4)
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- og:titleInfluencing LLM Output using logprobs and Token Distribution
- og:descriptionWhat if you could influence an LLM's output not by breaking its rules, but by bending its probabilities? In this deep-dive, we explore how small changes in user input (down to a single token) can shift the balance between “true” and “false”, triggering radically different completions.
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- twitter:titleInfluencing LLM Output using logprobs and Token Distribution
- twitter:descriptionWhat if you could influence an LLM's output not by breaking its rules, but by bending its probabilities? In this deep-dive, we explore how small changes in user input (down to a single token) can shift the balance between “true” and “false”, triggering radically different completions.
- twitter:urlhttps://blog.sicuranext.com/influencing-llm-output-using-logprobs-and-token-distribution/
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