Training Language Models to Generate Text with Citations via Fine-grained Rewards

The paper presents a novel training method for Large Language Models (LLMs) to generate text with accurate and relevant citations to external sources. The authors use fine-grained rewards, based on the correctness and relevance of citations, to improve the quality of generated text. The method outperforms baseline models, even surpassing GPT-3.5-turbo, in experiments conducted on Question Answering datasets from the ALCE benchmark and EXPERTQA.

Despite the promising results, the method has limitations such as computational intensity and questions around scalability beyond the specific dataset used. Further research is needed to explore its generalization, robustness to adversarial attacks, and real-world performance. Nonetheless, this approach represents a significant advancement in citation-aware language modeling, potentially benefiting applications like academic writing, journalism, and knowledge summarization.

Key takeaways:

The paper presents a method for training language models to generate text with accurate citations to external sources, using fine-grained rewards based on evaluating the correctness and relevance of citations.
The authors demonstrate improvements in citation quality and faithfulness to source material compared to baseline language models.
The training process is computationally intensive and there are open questions about how to scale this approach to broader domains.
This work has the potential to enable more reliable and trustworthy text generation in applications like academic writing, journalism, and knowledge summarization.

Training Language Models to Generate Text with Citations via Fine-grained Rewards | AI Research Paper Details

Key takeaways:

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