LLM Intent And Slot Extraction For Unknown Utterances
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional intent and slot extraction techniques in natural language processing are labor-intensive, prone to errors, and not scalable, requiring extensive manual labeling and struggling with unknown utterances.
Innovation Solution
A pre-train, prompt, and predict paradigm using a large language model (LLM) that generalizes intent and slot extraction by leveraging a limited set of semantically related phrases and contextual information, reducing manual input and improving flexibility and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised machine learning techniques with heavy feature engineering are used for intent detection, then the system can achieve accurate classification of known utterances, but the process becomes very tedious, time-consuming, and not scalable due to requiring manual tagging of slots in IOB format and curating massive labeled datasets
Solution Approach 1:
The system enables self-service by allowing the LLM to automatically generate slot extraction patterns and intent detection logic without requiring manual IOB tagging. The model processes utterances and autonomously identifies slots and intents, eliminating the need for tedious manual annotation while maintaining high accuracy
Solution Approach 2:
The approach performs preliminary action by pre-training the LLM on diverse language data before deployment. This pre-training equips the model with general language understanding capabilities, allowing it to handle intent detection and slot extraction without requiring extensive task-specific manual labeling afterward
2Ease of operation
If rule-based regular expressions are used for intent detection, then the system can provide interpretable and controllable pattern matching, but the mechanism requires brainstorming complex regular expressions and cannot handle unknown or varied utterance formulations
Solution Approach 1:
The system changes parameters by transitioning from fixed regular expression patterns to flexible LLM-based semantic understanding. The LLM can adapt to different utterance formulations by changing its interpretation parameters based on contextual understanding, enabling it to handle both known and unknown utterances while maintaining controllability through prompt engineering
3Adaptability or versatility
If extensive manual labeling and feature engineering are performed to improve intent detection coverage, then the system can recognize more utterance variations, but the complexity and resource requirements increase significantly
Solution Approach 1:
The LLM serves multiple functions simultaneously: it performs intent classification, slot extraction, and pattern generation without requiring separate systems for each task. This multi-functionality reduces overall system complexity while maintaining high adaptability to various utterance formulations through a single unified model
Data Source
AI summary
Techniques for performing contextualized intent and slot extraction using a large language model (LLM) are disclosed. The LLM is generally pre-trained on an arbitrary corpus of language training data. A prompt is provided to the LLM. This prompt includes a limited number of prompt phrases. The prompt phrases share a semantic relationship with one another. A spoken utterance is recorded and then converted to text, resulting in generation of a transcription. The transcription is provided to the LLM. The LLM extracts, from the transcription, an extracted intent and an extracted slot. The extracted intent is determined to be related to a prompt-described intent that was included in the prompt. The prompt is supplemented by adding the extracted intent and the extracted slot to the prompt, resulting in the extracted intent being identified as sharing the semantic relationship with the other prompt phrases in the prompt.