Natural Language Generation (NLG), a subfield of artificial intelligence, focuses on generating coherent and fluent text from data. While NLG is revolutionizing many areas, such as journalism, data analysis, and customer service, it's important to understand its limitations and the challenges it faces. In this article, we'll examine these limitations, providing examples and insights to underscore the current state of NLG technology.
1. Dependence on Quality Data
NLG systems depend heavily on the quality and consistency of the data they are fed. If the data input is incorrect, incomplete, or inconsistent, the output generated by NLG will be flawed. For instance, if an NLG system is used to create a financial report, and the underlying financial data is incorrect, the generated report will inevitably contain inaccuracies.
Moreover, NLG models trained on biased data can perpetuate and even amplify these biases, leading to unfair or inappropriate output. Google's language model, for instance, was criticized for generating sexist or racially biased text due to biased training data.
2. Lack of Understanding and Reasoning
NLG systems can generate human-like text, but they lack genuine understanding and reasoning. They can't infer the deeper meaning behind texts or make logical conclusions like humans can. For instance, an NLG system might generate a weather report stating, "It will be raining and sunny at the same time," because it lacks the understanding that these two weather conditions are usually mutually exclusive.
3. Difficulty Handling Ambiguity
Language is inherently ambiguous, with words often having multiple meanings depending on context. NLG systems can struggle to handle this ambiguity. For example, the word "bank" can refer to a financial institution or the side of a river. If an NLG system does not have enough context or if its training data does not adequately address this ambiguity, it might misuse such words.
4. Generation of Inappropriate Content
NLG systems have been known to generate inappropriate or harmful content. This is a concern in any NLG application but is especially crucial when NLG is used in customer-facing applications like chatbots or virtual assistants. For example, Microsoft's AI chatbot, Tay, was taken offline after it started generating offensive tweets, showcasing this limitation. 5. Limited Creativity and Nuance
While NLG systems can mimic human-like writing, they fall short in creativity and nuance. They can't generate original ideas, and their ability to use metaphors, humor, or other nuanced forms of language is limited. NLG systems can create text that is grammatically correct and coherent, but it often lacks the creativity and richness that characterizes human writing.
6. Privacy and Ethical Considerations
NLG systems often use personal data to generate text, raising privacy concerns. It's crucial to ensure that NLG systems comply with data protection regulations and respect user privacy. Ethical considerations also arise when using NLG. For example, if an NLG system is used to generate news articles, it might be necessary to inform readers that the content was generated by an AI, maintaining transparency.
7. Lack of Standardization and Evaluation Metrics
NLG lacks standardized benchmarks and evaluation metrics, making it challenging to compare different NLG systems or measure their performance objectively. Current evaluation metrics, like BLEU or ROUGE, focus on aspects like n-gram overlap but do not fully capture the fluency, coherence, or relevance of the generated text.
NLG is a powerful technology that holds great promise, but it is not without its limitations and challenges. It's crucial for practitioners and users to understand these limitations and work towards addressing them. Possible avenues for overcoming these limitations include developing better training datasets, improving algorithms for better context understanding, and establishing ethical guidelines and standards for NLG use.
Despite these challenges, NLG has the potential to dramatically improve efficiency and decision-making in many fields. While it is not a magic bullet, with thoughtful use and ongoing development, NLG can become a powerful tool in our AI toolkit.
As NLG continues to evolve, the focus should not only be on improving its capabilities but also on addressing its limitations and ensuring that it is used responsibly and ethically. By doing so, we can harness the potential of NLG while mitigating its risks, paving the way for a future where NLG is not just powerful and efficient, but also reliable, fair, and trustworthy.
Hovy, D. and Spruit, S. L. (2016). The Social Impact of Natural Language Processing. [Online] Available at: https://www.aclweb.org/anthology/P16-2096/
Microsoft. (2016). Learning from Tay’s introduction. [Online] Available at: https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/
Gartner. (2022). Natural Language Generation (NLG) Market Guide. [Online] Available at: https://www.gartner.com/en/documents/3980335/market-guide-for-natural-language-generation-technologies
Bias in NLG. (2023). Understanding Bias in NLG. [Online] Available at: https://arxiv.org/abs/2301.04967