Investigating Factuality in Long-Form Text Generation: The Roles of Self-Known and Self-Unknown

• URL: http://arxiv.org/abs/2411.15993v1
• Date: Sun, 24 Nov 2024 22:06:26 GMT
• Title: Investigating Factuality in Long-Form Text Generation: The Roles of Self-Known and Self-Unknown
• Authors: Lifu Tu, Rui Meng, Shafiq Joty, Yingbo Zhou, Semih Yavuz,
• Abstract summary: We investigate the factuality of long-form text generation across various large language models (LLMs) Our analysis reveals that factuality scores tend to decline in later sentences of the generated text, accompanied by a rise in the number of unsupported claims. We find a correlation between higher Self-Known scores and improved factuality, while higher Self-Unknown scores are associated with lower factuality.
• Score: 55.91887554462312
• License:
• Abstract: Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false information, especially in long-form generation. In this work, we investigates the factuality of long-form text generation across various large language models (LLMs), including GPT-4, Gemini-1.5-Pro, Claude-3-Opus, Llama-3-70B, and Mistral. Our analysis reveals that factuality scores tend to decline in later sentences of the generated text, accompanied by a rise in the number of unsupported claims. Furthermore, we explore the effectiveness of different evaluation settings to assess whether LLMs can accurately judge the correctness of their own outputs: Self-Known (the percentage of supported atomic claims, decomposed from LLM outputs, that the corresponding LLMs judge as correct) and Self-Unknown (the percentage of unsupported atomic claims that the corresponding LLMs judge as incorrect). The results indicate that even advanced models like GPT-4 and Gemini-1.5-Pro fail to achieve perfect Self-Known scores, while their Self-Unknown scores remain notably above zero, reflecting ongoing uncertainty in their self-assessments. Moreover, we find a correlation between higher Self-Known scores and improved factuality, while higher Self-Unknown scores are associated with lower factuality. Interestingly, even without significant changes in the models' self-judgment (Self-Known and Self-Unknown), the number of unsupported claims can increases, likely as an artifact of long-form generation. These findings show the limitations of current LLMs in long-form generation, and provide valuable insights for improving factuality in long-form text generation.

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