Table-Reasoning Language Models Without Logical-Form Generation
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Solution Overview
Problem
Existing natural language processing (NLP) models require labor-intensive supervised training data and weak supervision methods lead to spurious logical forms and unpredictable performance due to false associations.
Innovation Solution
Pre-training a neural-network-based language model using masked-language modeling tasks and counterfactual statements from unlabeled knowledge corpora, followed by fine-tuning with questions, answers, and tables, without generating logical forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If semantic processing methods are used to translate questions into logical forms, then the model can query tables for answers, but the training process becomes labor intensive and expensive due to the need for supervised training data
Solution Approach 1:
The system automatically generates training data by extracting questions and answers from tables without human intervention. The table-to-sql generator creates synthetic training examples by parsing table structures and generating corresponding queries, allowing the system to train itself without requiring manual annotation of training data.
Solution Approach 2:
The system creates synthetic training data by copying and transforming existing table structures into question-answer pairs. It generates virtual training examples that replicate the structure and content of real tables, enabling the model to learn from synthesized data rather than requiring original supervised data.
2Productivity
If weak supervision methods are used to train models with question-answer pairs only, then training becomes less labor intensive, but the model generates spurious logical forms and performs unpredictably due to false associations
Solution Approach 1:
The system incorporates feedback mechanisms where the table-to-sql generator continuously refines its output based on the actual table structure and content. It validates generated queries against the table schema and adjusts its parsing logic to ensure logical forms are syntactically correct and semantically meaningful, preventing false associations.
Solution Approach 2:
The system performs preliminary parsing and validation of table structures before generating training data. It pre-processes table information to identify valid fields, data types, and relationships, ensuring that only correct and meaningful logical forms are generated for training, thereby preventing spurious patterns from entering the training set.
3Device complexity
If the model architecture is simplified to reason directly over tables without logical forms, then the architecture becomes simpler and training is easier, but the model must handle multiple types of questions and table structures simultaneously
Solution Approach 1:
The language model is designed to perform multiple functions: it can answer direct lookup questions, perform aggregations (sum, average, count), handle filtering and sorting operations, and reason about table relationships all within a single unified architecture. The model learns to handle diverse question types through a common representation framework that processes tables and queries simultaneously.
Data Source
AI summary
Systems and methods for pre-training and fine-tuning of neural-network-based language models to reason directly over tables without generating logical forms. In some examples, a language model can be pre-trained using masked-language modeling tasks synthetically generated from tables pulled from a knowledge corpus. In some examples, the language model may be further pre-trained using pairs of counterfactual statements generated from those tables, and/or one or more statements that compare selected data from those tables. The language model may then be fine-tuned using examples that include only a question, an answer, and a table, allowing fine-tuning examples to be harvested directly from existing benchmark datasets or synthetically generated.