The Power and Pitfalls of Language Models in Generating Structured Text
Language models have become increasingly sophisticated, enabling them to generate text that adheres to specific structures and formats. This capability has found applications in various fields, including natural language understanding, code generation, and data extraction. However, there are both advantages and potential drawbacks to using language models for generating structured text.
One major advantage of language models (LLMs) lies in their calibrated probability distribution, which allows for the generation of diverse yet coherent responses. This means that an LLM can provide different valid outputs for a given prompt, each with a specific likelihood. However, a technique that aims to improve generation frequency can undermine this benefit.
The issue arises when a response has multiple equally valid outputs but the technique skews the probabilities in favor of certain outputs. For example, if the possible outputs of an LLM are “hello world,” “food,” “hello,” and “good day,” and all are equally probable, a technique that samples outputs until one passes certain grammar rules may generate “hello world” twice as frequently as “good day.” This imbalance in output distribution may be undesirable in certain contexts.
Another concern is that generating a valid response from an unlikely answer prefix may compound errors in autoregressive models. While the technique may construct a valid response, the resulting output may still contain factual errors or fail to adhere to a specified schema. This can be particularly problematic when the error rate relates to schema error rate, as errors in one aspect of the output may bleed into other characteristics.
The debate arises as to whether such issues can be adequately addressed through instruction-tuning and post-hoc guided generation. Instruction-tuning, a process of fine-tuning LLMs for specific use cases, can improve their performance, but it is not a foolproof solution. Many believe that guided generation with grammars or other structural constraints may provide a more elegant and reliable solution for generating structured text.
Proponents argue that using language models as a base and incorporating instructions and constraints can yield better results than relying solely on LLMs. For example, providing clear schema guidelines or using pre-defined grammars can enhance the generation of structured data. Moreover, there is a belief that incorporating humans in the generation process, such as through property-based testing or manual input, can improve the quality and accuracy of the generated text.
While there are ongoing debates and concerns surrounding the use of LLMs in generating structured text, it is clear that different approaches have their merits and limitations. Instruction-tuning can improve the specific use cases for LLMs, but it may not fully mitigate the challenges posed by probabilistic generation and schema adherence. Meanwhile, incorporating grammars or human input can enhance the accuracy and reliability of structured text generation.
In conclusion, the power of language models in generating structured text lies in their calibrated probability distribution and the ability to produce diverse valid responses. However, techniques aimed at improving generation frequency may disrupt this balance, compromising the reliability and coherence of the output. Combining LLMs with instruction-tuning, grammars, or human input may offer more robust solutions for generating structured text, addressing the challenges of probabilistic generation and schema adherence.
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Author Eliza Ng