Enabling Verifiable Text Generation with Symbolic References in Large Language Models

Core Concepts

Symbolically grounded generation (SymGen) is a simple approach that enables easier manual verification of large language model (LLM) outputs by interleaving generated text with explicit symbolic references to fields present in the conditioning data.

Abstract

The paper proposes symbolically grounded generation (SymGen) as a method to enable easier manual verification of large language model (LLM) outputs. SymGen prompts an LLM to generate text that interleaves the regular output with explicit symbolic references to fields present in some conditioning data, such as a table in JSON format. The key insights are: SymGen can maintain the fluency and factuality of LLM-generated text while providing symbolic references that can be used to display the provenance of different spans of text. Experiments on data-to-text generation and question answering tasks show that LLMs can directly output text with accurate symbolic references. A human study finds that such annotations can streamline the process of manually verifying machine-generated text, reducing the average verification time by 20%. SymGen is compared to a baseline of standard LLM-generated text without any annotations. The results indicate that SymGen can retain the textual quality of the baseline while providing the additional benefit of improved verifiability.

Stats

The visitor.city scored 30 points. The home team scored 90 total points. The game had 5 total quarters. The home team had 18 total rebounds.

Quotes

"SymGen imbues spans of generated text (highlighted in blue) with symbolic references to the source data, enabling easier verification: e.g., when hovering over a span, the number "30" displays a tooltip and link (highlighted in yellow) indicating the value it is referencing." "Across a range of data-to-text and question-answering experiments, we find that LLMs are able to directly output text that makes use of accurate symbolic references while maintaining fluency and factuality." "In a human study we further find that such annotations can streamline human verification of machine-generated text."

Key Insights Distilled From

Towards Verifiable Text Generation with Symbolic References

by Lucas Torrob... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2311.09188.pdf

Deeper Inquiries

How could SymGen be extended to handle more complex data structures beyond JSON, such as relational databases or knowledge graphs?

SymGen's approach of using symbolic references to link generated text to structured data can be extended to handle more complex data structures beyond JSON by adapting the parsing and rendering mechanisms to accommodate the specific structure of relational databases or knowledge graphs. Here are some ways this extension could be achieved: Custom Parsing Logic: Develop custom parsing logic tailored to the structure of relational databases or knowledge graphs. This logic would need to understand the relationships between different entities, attributes, and values in these data structures. Graph-based Representations: Represent the relational database or knowledge graph as a graph data structure, where nodes represent entities and edges represent relationships between them. Symbolic references in the generated text can then point to specific nodes or edges in the graph. Query Language Integration: Integrate query languages like SQL or SPARQL into the SymGen framework to allow for more complex retrieval of data from relational databases or knowledge graphs. Symbolic references can then be used to refer to specific query results. Ontology Mapping: Map the entities and relationships in the relational database or knowledge graph to a common ontology that the language model understands. This mapping can help in generating more coherent and accurate text with symbolic references. Hierarchical Structures: Handle hierarchical structures present in knowledge graphs by incorporating nested symbolic references that traverse the hierarchy of nodes and edges. By incorporating these strategies, SymGen can be extended to effectively handle more complex data structures like relational databases and knowledge graphs, enabling the generation of verifiable text that is closely tied to the underlying structured data.

What are the potential limitations of SymGen in terms of the types of errors it can detect, and how could it be combined with other verification techniques?

SymGen, while effective in linking generated text to structured data through symbolic references, may have limitations in detecting certain types of errors. Some potential limitations include: Semantic Errors: SymGen may struggle to detect errors related to the semantic accuracy of the generated text, such as incorrect interpretations of the data or logical inconsistencies. Contextual Errors: It may not be able to identify errors that arise from the broader context of the information being presented, such as missing background information or incorrect assumptions. Ambiguity: SymGen may face challenges in handling ambiguous data or text, leading to errors in interpretation and referencing. To overcome these limitations and enhance the verification process, SymGen can be combined with other verification techniques such as: Cross-Validation: Use cross-validation techniques to compare the output generated by SymGen with multiple independent sources or models to identify discrepancies and errors. Human-in-the-Loop Verification: Incorporate human reviewers into the verification process to provide qualitative assessments and identify errors that may be missed by automated techniques. Rule-Based Checks: Implement rule-based checks to verify specific aspects of the generated text, such as consistency checks, fact-checking, or logical reasoning. Knowledge Base Integration: Integrate external knowledge bases or fact-checking databases to validate the information presented in the generated text against known facts and data. By combining SymGen with these verification techniques, it is possible to enhance the error detection capabilities and ensure the accuracy and reliability of the generated text.

How might SymGen's approach of interleaving natural language with symbolic references inspire new ways of building human-AI collaborative systems for tasks that require both natural language understanding and structured reasoning?

SymGen's approach of interleaving natural language with symbolic references can inspire new ways of building human-AI collaborative systems by fostering a more transparent and interpretable interaction between humans and AI. Here are some ways this approach could influence the development of collaborative systems: Enhanced Explainability: By incorporating symbolic references, AI systems can provide detailed explanations and justifications for their outputs, making the reasoning process more transparent to human users. Interactive Verification: Human users can interact with the AI system by exploring the symbolic references to understand how the generated text is linked to the underlying data, enabling collaborative verification of the output. Error Correction: Human users can correct errors or provide feedback by directly manipulating the symbolic references, guiding the AI system to generate more accurate and contextually relevant text. Knowledge Transfer: The use of symbolic references can facilitate knowledge transfer between humans and AI systems, allowing for a more seamless exchange of information and insights. Task Decomposition: SymGen's approach encourages breaking down complex tasks into smaller, more manageable subtasks represented by symbolic references, enabling a collaborative division of labor between humans and AI. By leveraging SymGen's methodology of combining natural language with symbolic references, human-AI collaborative systems can be designed to enhance communication, understanding, and cooperation in tasks that require a blend of natural language understanding and structured reasoning.