LLM Dialog Context Curation for Harmful Response Prevention

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Solution Overview

Problem

Large language models (LLMs) generate undesirable natural language (NL) outputs due to unmanaged dialog contexts, which can lead to inappropriate or harmful responses, hindering effective human-computer interaction.

Innovation Solution

A system that manages dialog contexts by selectively 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

1Reliability

If the LLM processes the complete dialog context to generate responses, then the relevance and coherence of the response is improved, but the risk of generating harmful or undesirable outputs increases

Engineering Contradiction:

Improvesafety of LLM outputVSAvoidharmful information in response

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The dialog context is segmented into individual turns, and each turn is independently evaluated using an assurance score. This allows selective modification of specific portions of the context rather than treating the entire context uniformly, enabling harmful content to be isolated and addressed without discarding useful contextual information from other turns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different parts of the dialog context are treated differently based on their assurance scores. Turns with high assurance scores are retained as-is, while turns with low assurance scores are modified or filtered. This local differentiation allows the system to maintain safety where needed while preserving conversational flow and relevance in safe areas.

Inventive Principle:

Principle #3Local quality

2Reliability

If the dialog context is modified to prevent harmful outputs, then the safety of the response is improved, but the contextual accuracy and relevance may deteriorate

Engineering Contradiction:

Improvesafety of LLM outputVSAvoidcontextual accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The system modifies parameters of the dialog context (such as filtering, redacting, or modifying specific turns) based on assurance scores. By adjusting these parameters dynamically based on the risk level of each context turn, the system can prevent harmful outputs while maintaining the overall accuracy and relevance of the conversation context.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If assurance scores are calculated for all dialog turns, then the safety monitoring is improved, but the processing time and computational cost increase

Engineering Contradiction:

Improvesafety monitoringVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

Instead of uniformly processing the entire dialog context, the system applies partial action by calculating assurance scores selectively for turns that may contain harmful content. The system can skip or reduce processing for turns that appear safe based on preliminary analysis, thereby reducing overall processing time while maintaining adequate safety monitoring.

Inventive Principle:

Principle #16Partial or excessive action

4Reliability

If the dialog context is restricted to prevent harmful content, then the safety is improved, but the quality and usefulness of the response may deteriorate

Engineering Contradiction:

Improvesafety of LLM outputVSAvoidquality of response

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system converts potentially harmful context turns into beneficial filtering opportunities. By identifying turns with low assurance scores, the system can selectively modify or remove only those specific turns while preserving the rest of the context. This approach maintains safety by removing harmful content while preserving the quality and usefulness of the overall conversation context for generating accurate responses.

Inventive Principle:

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

Data Source

PatentUS20250356133A1Dialog management for large language model-based (LLM-based) dialogs

Publication Date: 2025.11.20 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.