LLM Dialog Context Curation for Harmful Output Prevention

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Solution Overview

Problem

Large language models (LLMs) generate undesirable natural language (NL) outputs when processing dialog contexts, which can lead to unintended consequences such as providing harmful information or failing to progress human-computer interactions effectively.

Innovation Solution

Implementing a system that manages dialog contexts by modifying or curating them based on assurance scores, restricting content, or adjusting output thresholds to ensure safe and contextually relevant responses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the LLM processes the complete dialog context to generate responsive NL output, then the dialogue relevance and contextual understanding are improved, but harmful information may be generated and safety deteriorates

Engineering Contradiction:

Improvedialogue relevanceVSAvoidharmful information generation

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The dialog context is segmented into multiple portions, and the system selectively processes only the relevant portions while excluding harmful content. This allows the LLM to maintain dialogue relevance through contextual understanding while preventing harmful information generation by blocking specific segments of the dialog context from being processed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Harmful content is extracted and removed from the dialog context before processing. The system identifies and takes out harmful portions of the dialog context, separating them from the useful contextual information, thereby enabling the LLM to generate safe responses while maintaining dialogue relevance through the remaining clean context.

Inventive Principle:

Principle #2Taking out (Extraction)

2Object-affected harmful factors

If the dialog context is modified to prevent harmful outputs, then safety is improved, but the dialogue flow and interaction progress may be disrupted

Engineering Contradiction:

ImprovesafetyVSAvoiddialogue flow

Core Design Contradiction:

Object-affected harmful factorsVSEase of operation

Solution Approach 1:

The system converts harmful dialog context portions into beneficial safety signals by identifying harmful patterns and transforming them into triggers for protective actions. This allows the system to maintain safety while preserving dialogue flow, as the harmful content is not simply blocked but transformed into a mechanism for safe interaction continuation.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Solution Approach 2:

An intermediary filtering mechanism is introduced between the dialog context and the LLM processing stage. This intermediary component moderates the interaction by selectively allowing or blocking content based on safety criteria, thereby ensuring safety while maintaining natural dialogue flow through intelligent mediation rather than simple blocking.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of information

If the system processes every NL input and builds comprehensive dialog context, then the information completeness is improved, but the processing time and computational resources increase

Engineering Contradiction:

Improveinformation completenessVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system extracts only the necessary information from each NL input and builds a streamlined dialog context containing only relevant content. By taking out and retaining only essential information rather than processing every detail, the system maintains information completeness for meaningful dialogue while reducing processing time and computational resource consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The dialog context management applies local quality by treating different portions of the dialog context differently based on their relevance and importance. The system prioritizes processing and retaining high-quality relevant information while discarding or minimizing processing of less relevant content, thereby maintaining essential information completeness while optimizing processing efficiency.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12406147B2Dialog management for large language model- based (LLM-based) dialogs

Publication Date: 2025.09.02 GOOGLE LLC

AI summary

Implementations relate to dialog management of a large language model (LLM) utilized in generating natural language (NL) output during an ongoing dialog. Processor(s) of a system can: receive NL based input as part of the ongoing dialog, generate NL based output utilizing the LLM, and cause the NL based output to be rendered. Further, the processor(s) can receive subsequent NL based input as part of the ongoing dialog. In some implementations, the processor(s) can determine whether to modify a corresponding dialog context in generating subsequent NL based output, and modify the corresponding dialog context accordingly. For example, the processor(s) can restrict the corresponding dialog context, or supplant the corresponding dialog context with a corresponding curated dialog context. In additional or alternative implementations, the processor(s) can modify a corresponding NL based output threshold utilized in generating the subsequent NL based response to ensure the resulting NL based output is desirable.