Sign In

Aligning Large Language Models to Quote Verbatim from High-Quality Pre-Training Data for Improved Verifiability

Core Concepts
Developing a method called QUOTE-TUNING that aligns large language models to quote verbatim from high-quality pre-training data, enabling more verifiable and truthful generations.
The paper proposes QUOTE-TUNING, a method to align large language models (LLMs) to quote verbatim from high-quality pre-training data, such as Wikipedia. The key insights are: LLMs have memorized a significant amount of information from their pre-training data, which can be leveraged to generate verifiable quotes. QUOTE-TUNING works by (1) sampling multiple responses from a pre-trained LLM, (2) constructing a preference dataset that favors responses with more quotes, and (3) optimizing the LLM to generate more quoted text using preference optimization algorithms. Experiments on long-form question answering and open-ended text completion show that QUOTE-TUNING significantly increases the percentage of quoted text from 55% to 130% compared to un-tuned models, while maintaining or improving generation quality. Further analysis demonstrates that QUOTE-TUNING also enhances the truthfulness of generated text, even though it is not explicitly optimized for truthfulness. QUOTE-TUNING provides a verifiable-by-design approach that leverages the parametric knowledge of LLMs, complementing existing methods that rely on external knowledge bases.
LLMs are pre-trained on internet-scale data, a subset of which contains high-quality, reliable information. Pre-trained LLMs have memorized a wide range of content from their pre-training data. QUOTE-TUNING can increase the percentage of LLM generation quoted verbatim from high-quality pre-training documents by 55% to 130% relative to un-tuned models.
"Trust, but verify." "Verifiability allows users to uncover the competency of LLMs and calibrate user trust, a crucial aspect of building trustworthy human-machine relationships."

Key Insights Distilled From

by Jingyu Zhang... at 04-08-2024
Verifiable by Design

Deeper Inquiries

How can QUOTE-TUNING be extended to simultaneously maximize the rate and length of quoting from pre-training data?

To extend QUOTE-TUNING to simultaneously maximize the rate and length of quoting from pre-training data, several adjustments can be made to the algorithm: Length Regularization: Introduce a more sophisticated length regularization mechanism that considers the distribution of quoted segments across different lengths. By balancing the rate and length of quoting, the algorithm can prioritize longer, more informative quotes while still maintaining a high quoting rate. Multi-Step Optimization: Implement a multi-step optimization process where the algorithm first maximizes the quoting rate and then fine-tunes the model to generate longer quotes without compromising the quoting rate. This iterative approach can help strike a balance between rate and length. Dynamic Hyperparameters: Introduce dynamic hyperparameters that adapt based on the distribution of quoted segments during training. By adjusting the hyperparameters based on the model's performance, QUOTE-TUNING can optimize both rate and length effectively. Segment-Level Quoting: Instead of focusing on the entire response, QUOTE-TUNING can operate at a segment level, encouraging the model to generate longer quoted segments while maintaining a high quoting rate. This approach can lead to more informative and verifiable responses. By incorporating these enhancements, QUOTE-TUNING can achieve a more balanced approach to quoting, maximizing both the rate and length of quoted segments from pre-training data.

How can QUOTE-TUNING be applied in the context of instruction-tuned language models, where a diverse set of tasks are present?

In the context of instruction-tuned language models with a diverse set of tasks, QUOTE-TUNING can be adapted and applied in the following ways: Task-Specific Quoting: Customize QUOTE-TUNING for each specific task by providing task-specific corpora for quoting. By aligning the model to quote from task-relevant sources, QUOTE-TUNING can enhance the verifiability and trustworthiness of the model's responses across diverse tasks. Multi-Task Training: Incorporate multi-task training into QUOTE-TUNING, where the model is trained on a variety of tasks simultaneously. This approach can help the model learn to quote accurately across different domains and tasks, improving its ability to generate verifiable responses. Fine-Tuning with Task Instructions: Integrate task instructions into the fine-tuning process of QUOTE-TUNING. By aligning the model's quoting behavior with the specific instructions provided for each task, QUOTE-TUNING can generate responses that are not only verifiable but also tailored to the task requirements. Dynamic Corpus Selection: Implement a dynamic corpus selection mechanism that adapts to the task at hand. By selecting task-specific corpora for quoting based on the input task, QUOTE-TUNING can ensure that the model quotes relevant and accurate information for each task. By customizing QUOTE-TUNING for instruction-tuned language models and incorporating task-specific adaptations, the algorithm can effectively enhance the verifiability and trustworthiness of responses across a diverse range of tasks.

Can QUOTE-TUNED models be combined with retrieval-augmented generation techniques to further improve verifiability and truthfulness?

Combining QUOTE-TUNED models with retrieval-augmented generation techniques can indeed enhance verifiability and truthfulness in the following ways: Enhanced Fact-Checking: By integrating retrieval-augmented generation techniques, QUOTE-TUNED models can retrieve and incorporate relevant information from external sources to support their generated responses. This approach can improve fact-checking capabilities and ensure that the generated content is accurate and verifiable. Cross-Validation: Retrieval-augmented generation can be used to cross-validate the quoted information generated by QUOTE-TUNED models. By comparing the generated quotes with information retrieved from external sources, the model can ensure consistency and accuracy in its responses. Diverse Perspectives: Incorporating retrieval-augmented generation allows QUOTE-TUNED models to access a diverse range of sources and perspectives. This can help in presenting a more comprehensive and balanced view in the generated content, enhancing truthfulness and credibility. Fine-Tuning with External Data: By leveraging retrieval-augmented generation techniques during fine-tuning, QUOTE-TUNED models can be trained on a more extensive and diverse set of data. This can improve the model's understanding of different topics and enhance its ability to generate verifiable and truthful responses. Overall, the combination of QUOTE-TUNED models with retrieval-augmented generation techniques can synergistically improve the verifiability and truthfulness of language model outputs by incorporating external information and diverse perspectives into the generation process.