The blog post explores using traditional machine learning (specifically, decision trees) to interpret and refine the output of less capable or "dumb" Large Language Models (LLMs). The author describes a scenario where an LLM is tasked with classifying customer service tickets, but its performance is unreliable. Instead of relying solely on the LLM's classification, a decision tree model is trained on the LLM's output (probabilities for each classification) along with other readily available features of the ticket, like length and sentiment. This hybrid approach leverages the LLM's initial analysis while allowing the decision tree to correct inaccuracies and improve overall classification performance, ultimately demonstrating how simpler models can bolster the effectiveness of flawed LLMs in practical applications.
Doug, the author of the blog post "Coping with dumb LLMs using classic ML," explores the inherent unreliability of Large Language Models (LLMs) and proposes a method to mitigate their shortcomings by leveraging traditional machine learning techniques, specifically decision trees. He illustrates this concept with a practical example: determining whether a piece of text generated by an LLM constitutes a valid legal judgment.
Doug begins by acknowledging the impressive capabilities of LLMs in generating human-like text, yet emphasizes their fundamental flaw: they lack true understanding and reasoning abilities. Consequently, while an LLM might produce text that superficially resembles a legal judgment, it may be nonsensical or contain critical errors upon closer inspection. This unreliability renders LLMs unsuitable for tasks requiring precise and logically sound outputs, such as drafting legal documents.
To address this issue, Doug introduces the idea of employing a "judge" to evaluate the output of the LLM. This judge, rather than being a human expert, is implemented as a decision tree trained on a dataset of genuine and fabricated legal judgments. The decision tree learns to identify patterns and features that distinguish authentic judgments from the LLM-generated imitations. These features could include aspects like the structure of the text, the specific terminology used, the presence of citations, and the overall coherence of the arguments presented.
The blog post details the process of training the decision tree using the scikit-learn library in Python. Doug meticulously explains the steps involved in preparing the dataset, selecting appropriate features, training the model, and evaluating its performance. He highlights the importance of using a balanced dataset containing both real and fake judgments to ensure the model learns to differentiate effectively between them.
Doug further elaborates on the specific features used to train the decision tree. These include metrics like the frequency of certain keywords associated with legal language, the overall length of the document, and the complexity of the sentences used. He demonstrates how these features can be extracted from the text and used as input to the decision tree model.
The results presented in the blog post demonstrate the effectiveness of this approach. The trained decision tree achieves a reasonable level of accuracy in distinguishing between genuine legal judgments and those generated by the LLM. While not perfect, the judge provides a significant improvement over relying solely on the LLM's output.
Doug concludes by suggesting that this method can be generalized to other domains where the output of LLMs needs to be verified for accuracy and reliability. He argues that combining the generative power of LLMs with the discerning capabilities of classical machine learning models like decision trees offers a promising path towards harnessing the potential of LLMs while mitigating their inherent limitations. This hybrid approach allows for a more robust and trustworthy application of LLMs in various fields.
Summary of Comments ( 44 )
https://news.ycombinator.com/item?id=42790820
Hacker News users discuss the practicality and limitations of the proposed decision-tree approach to mitigate LLM "hallucinations." Some express skepticism about its scalability and maintainability, particularly with the rapid advancement of LLMs, suggesting that improving prompt engineering or incorporating retrieval mechanisms might be more effective. Others highlight the potential value of the decision tree for specific, well-defined tasks where accuracy is paramount and the domain is limited. The discussion also touches on the trade-off between complexity and performance, and the importance of understanding the underlying limitations of LLMs rather than relying on patches. A few commenters note the similarity to older expert systems and question if this represents a step back in AI development. Finally, some appreciate the author's honest exploration of alternative solutions, acknowledging that relying solely on improving LLM accuracy might not be the optimal path forward.
The Hacker News post titled "Coping with dumb LLMs using classic ML" (linking to an article about using decision trees to augment LLMs) has generated a modest discussion with several insightful comments.
One commenter points out that the approach described in the article, which involves using a decision tree to guide the LLM's output, isn't fundamentally different from prompt engineering. They argue that crafting a detailed prompt is essentially providing a structured set of rules, much like a decision tree. This comment highlights the blurred lines between different techniques for controlling LLM behavior, suggesting that "prompt engineering" might encompass a wider range of methods than typically assumed.
Another commenter raises the question of maintainability. They acknowledge the potential benefits of using decision trees for specific tasks but express concern about the long-term implications of managing and updating these trees as requirements evolve. They suggest that the complexity of maintaining a decision tree could outweigh its advantages in certain dynamic environments.
A further comment delves into the limitations of relying solely on the LLM's internal representations. The commenter argues that while LLMs can store and access a vast amount of information, they lack a reliable mechanism for consistently applying this knowledge in a structured manner. This comment reinforces the article's premise, suggesting that external structures like decision trees can help bridge this gap and improve the reliability of LLM outputs.
Another commenter draws a parallel with older symbolic AI techniques. They suggest that the approach of using decision trees with LLMs represents a return to these earlier methods, combining the strengths of both symbolic and statistical AI. This comment frames the discussion within a broader historical context of AI research.
Finally, a commenter questions the scalability of the proposed approach. They wonder how well the decision tree method would perform with more complex scenarios and larger datasets, expressing skepticism about its general applicability. This comment introduces an important consideration for practical implementations of the described technique.
Overall, the comments on Hacker News provide a valuable critique and extension of the article's core ideas. They raise important questions about the practicality, maintainability, and broader implications of using decision trees to enhance LLM performance, offering a nuanced perspective on the potential and limitations of this hybrid approach.