Natural Language Comparison Interpretation via Chain-of-Thought Reasoning
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Solution Overview
Problem
Interpreting and providing analytical responses for natural language comparisons during visual analysis remains challenging due to ambiguous language and subjective judgments, leading to varying interpretations among users.
Innovation Solution
An interactive interface using a machine-learning query parser and multi-step chain-of-thought reasoning prompting algorithm that interprets natural language comparison utterances, generates relevant visualizations, and provides text summaries, with affordances for users to refine ambiguous tokens and switch between visualization types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional parsers are used to interpret natural language comparisons, then the system can provide basic visualization responses, but the interpretation accuracy deteriorates due to ambiguous and vague language
Solution Approach 1:
The patent introduces an intermediary natural language processing layer between the conventional parser and the visualization system. This intermediary uses large language models to first interpret the ambiguous natural language comparison into a structured intermediate representation that captures the intended meaning, which then guides the visualization generation. This mediator resolves the ambiguity before it reaches the parser, improving both accuracy and consistency.
Solution Approach 2:
The system implements feedback mechanisms where the visualization response and its underlying data are fed back to the large language model to generate a summary description of the comparison. This feedback loop allows the system to verify its interpretation against the actual data and adjust its understanding, improving interpretation accuracy and consistency across multiple interactions.
2Measurement precision
If multi-step chain-of-thought reasoning prompting is used to improve interpretation accuracy, then the system can handle ambiguous language better, but the processing time increases
Solution Approach 1:
The system performs preliminary action by using the large language model to interpret the natural language comparison and identify key comparison elements before the actual visualization processing begins. This preliminary interpretation step structures the ambiguous input into a clear query format, so that subsequent processing steps work with already-parsed information rather than raw ambiguous text, reducing overall processing time while maintaining high accuracy.
Solution Approach 2:
The multi-step chain-of-thought reasoning is segmented into distinct processing stages: natural language interpretation, query formulation, data retrieval, visualization generation, and summary creation. Each stage processes a specific aspect of the comparison task independently, allowing for optimized processing at each step and avoiding the need to reprocess the entire query multiple times, thus managing processing time efficiently.
3Loss of information
If the system provides detailed text summaries with multi-step reasoning, then the transparency of comparison interpretation improves, but the complexity of the response increases
Solution Approach 1:
The detailed text summary is segmented into distinct components corresponding to each step of the chain-of-thought reasoning process. Each segment explains one specific aspect of the comparison interpretation (e.g., identifying comparison targets, selecting attributes, retrieving data, generating visualization). This segmentation makes the complex reasoning process more manageable and easier to follow, reducing perceived complexity while maintaining transparency.
Solution Approach 2:
The large language model acts as an intermediary that translates the complex internal reasoning processes into human-readable explanatory text. It mediates between the system's internal data structures and the user's need for transparent explanation, converting technical processing steps into natural language descriptions that are easy to understand while preserving the complete reasoning trail.
Data Source
AI summary
System, method and interface for interpreting natural language comparisons during visual analysis are provided. The system includes obtaining a natural language utterance that includes a comparison query and a dataset of attributes and values relevant to interpreting the comparison query. The system also includes interpreting the natural language utterance based on the dataset using multi-step chain-of-thought reasoning prompting to generate a response to the comparison query. The system also includes generating a visualization based on the response and a text summary describing the multi-step chain-of-thought reasoning for the comparison query.