Key Points

1. The paper introduces the concept of verifiable-by-design for language models, highlighting the importance of allowing humans to verify the correctness of large language models (LLMs). The paper addresses the limitations of recent efforts to increase verifiability through citations or post-hoc provenance and introduces Q UOTE -T UNING, a method that aligns LLMs to quote verbatim statements from trusted sources in pre-training data.

2. Q UOTE -T UNING demonstrates the feasibility of aligning LLMs to leverage memorized information and quote from pre-training data. It quantifies quoting against large corpora and uses the amount of quotes as an implicit reward signal to construct a synthetic preference dataset for quoting, without human annotation.

3. Experimental results show that Q UOTE -T UNING significantly increases the percentage of LLM generation quoted verbatim from high-quality pre-training documents, by 55% to 130% relative to untuned models, while maintaining response quality. This method is also applicable in different tasks and provides additional benefits to truthfulness.

4. The paper provides detailed experimental results on long-form question answering and open-ended text completion, showing that Q UOTE -T UNING significantly improves quoting and fluency, while maintaining the adequacy of generated answers.

5. Additionally, the paper explores the impact of preference optimization and analyzes the challenges and future directions for the Q UOTE -T UNING method and its application to other settings.

6. The research findings suggest that Q UOTE -T UNING can indirectly improve the truthfulness of LLM-generated responses and can be used for easier verification and building human-machine trust.

7. The paper highlights the significance of quoting from high-quality pre-training data, such as Wikipedia, in improving the verifiability and truthfulness of LLM generations, and discusses potential future applications and limitations of the Q UOTE -T UNING method.

8. The study also discusses the impact of preference data, memorization, and quoting as an interface for parametric knowledge, providing insights into the alignment of LLMs to human preferences and the implications for improved quoting and verification.

9. Overall, the paper contributes to the development of a method for increasing the verifiability and trustworthiness of LLMs by aligning them to quote from pre-training data, without the need for human annotation or external knowledge bases.

Summary

QUOTE-TUNING Methodology
The paper proposes a method called QUOTE-TUNING to increase the verifiability of large language models (LLMs) by aligning them to quote verbatim statements from trusted sources in their pre-training data. The method is designed to trivialize the verification process by developing models that can quote from trusted sources without the need for post-hoc provenance or citations. QUOTE-TUNING accomplishes this by quantifying quoting against large corpora and using the amount of quotes as an implicit reward signal to construct a synthetic preference dataset for quoting, without any human annotation. The target model is then aligned to quote using preference optimization algorithms.

Experimental Results
Experimental results show that QUOTE-TUNING significantly increases the percentage of LLM generation quoted verbatim from high-quality pre-training documents by 55% to 130% relative to untuned models while maintaining response quality. The method also generalizes quoting to out-of-domain data, is applicable in different tasks, and provides additional benefits to truthfulness. In addition, QUOTE-TUNING addresses the limitations of existing citation-based verification methods and has the potential to enhance the trustworthiness of LLM-generated text.

Detailed Insights and Discussion
The paper provides detailed insights into the QUOTE-TUNING method, its ability to align LLMs to quote from pre-training data, the experimental results of its implementation, and its potential in enhancing trustworthiness. It also discusses the limitations of existing verification methods and the potential of QUOTE-TUNING to address these limitations. Overall, the paper demonstrates how QUOTE-TUNING can play a crucial role in improving the verifiability of large language models and increasing trustworthiness in LLM-generated text.

Reference: https://arxiv.org/abs/2404.038...