US 2026/0039618 A1Published application

Synchronous and asynchronous content filtering

AssigneeSalesforce, Inc.

InventorChaney Lin

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Abstract

Techniques for filtering out undesirable generative machine learned model (or LLM) output are discussed herein. A system may receive a subset of an LLM output. That is, the system may stream the LLM output to a user device by receiving one or more tokens from the LLM and outputting such token(s) to a user device. However, prior to outputting the token(s) to the user device, the system may determine whether the token(s) include undesirable content that is to be blocked. The system may use synchronous blocking components (e.g., blocks the undesirable token(s) before such token(s) get output to the user device) and/or asynchronous blocking components (e.g., blocks the undesirable token(s) after the token(s) have been output to the user device) to filter out undesirable content. The synchronous and/or asynchronous blocking components may be designed to block one or more undesirable topics such as hateful speech, profanity, bias, toxicity, factualness, etc.

Claims20

Dependent claims

A system comprising:

one or more processors;
and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
receiving a subset of a response from a large language model (LLM);
causing, prior to outputting the subset, the subset to be sent to a first blocking detector and a second blocking detector that is different than the first blocking detector;
determining, based at least in part on the first blocking detector, a first result indicating whether to block the subset;
determining, based at least in part on the second blocking detector, a second result indicating whether to block subset;
determining, based at least in part on the first result and the second result, to output the subset of the response to a user device;
causing, in response to outputting the subset of the response and prior to an entirety of the response being output, the subset to be added to a buffer associated with a third blocking detector configured to determine whether to block the subset after the subset has been output to the user device;
determining, based at least in part on a status of the buffer, a third result indicating whether to block data within the buffer;
generating, in response to the entirety of the response from the LLM being output, a final result representing whether to block a portion of the response;
generating, based at least in part on the final result, modified data associated with the entirety of the response;
and
causing the modified data to be output.

11 more elements

One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:

receiving a subset of a response from a large language model (LLM);
causing, prior to outputting the subset, the subset to be sent to a first blocking detector;
determining, based at least in part on the first blocking detector, a first result indicating whether to block the subset;
determining, based at least in part on the first result, to output the subset of the response to a user device;
causing, in response to outputting the subset of the response and prior to an entirety of the response being output, the subset to be added to a buffer associated with a second blocking detector configured to determine whether to block the subset after the subset has been output to the user device;
determining, based at least in part on a status of the buffer, a second result indicating whether to block data within the buffer;
generating, in response to the entirety of the response from the LLM being output, a final result representing whether to block a portion of the response;
and
generating, based at least in part on the final result, modified data associated with the entirety of the response.

6 more elements

A method comprising:

receiving a subset of a response from a large language model (LLM);
causing, prior to outputting the subset, the subset to be sent to a first blocking detector;
determining, based at least in part on the first blocking detector, a first result indicating whether to block the subset;
determining, based at least in part on the first result, to output the subset of the response to a user device;
causing, in response to outputting the subset of the response and prior to an entirety of the response being output, the subset to be added to a buffer associated with a second blocking detector configured to determine whether to block the subset after the subset has been output to the user device;
determining, based at least in part on a status of the buffer, a second result indicating whether to block data within the buffer;
generating, in response to the entirety of the response from the LLM being output, a final result representing whether to block a portion of the response;
generating, based at least in part on the final result, modified data associated with the entirety of the response.

5 more elements

Dependencies are inferred from the claim text.