Verifiable By Design: Aligning Language Models To Quote From Pre-training Data
Zhang Jingyu, Marone Marc, Li Tianjian, Van Durme Benjamin, Khashabi Daniel. Arxiv 2024
[Paper]
To trust the fluent generations of large language models (LLMs), humans must be able to verify their correctness against trusted, external sources. Recent efforts, such as providing citations via retrieved documents or post-hoc provenance, enhance verifiability but still provide no guarantees on their correctness. To address these limitations, we tackle the verifiability goal with a different philosophy: trivializing the verification process by developing models that quote verbatim statements from trusted sources in pre-training data. We propose Quote-Tuning, and demonstrate it is feasible to align LLMs to provide quoted statements from data memorized during pre-training. The core of Quote-Tuning is a fast membership inference function (Marone and Van Durme, 2023) that efficiently verifies text against a trusted corpus. We leverage this tool to design a reward function to quantify quotes in model responses, which is then used to create a dataset for preference learning. Experimental results show that Quote-Tuning significantly increases verbatim quotes from high-quality pre-training documents by 55% to 130% relative to un-tuned models while maintaining response quality. Quote-Tuning also generalizes quoting to out-of-domain data, is applicable in different tasks, and provides additional benefits to truthfulness. Our method not only serves as a hassle-free method to increase quoting but also opens up avenues for improving LLM trustworthiness through better verifiability.
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