Stories with Tag Limitations of LLMs

• I genuinely don't understand why some people are still bullish about LLMs

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  Posted: 2025-03-27 21:22:42

  Verbose tl;dr

  The author expresses skepticism about the current hype surrounding Large Language Models (LLMs). They argue that LLMs are fundamentally glorified sentence completion machines, lacking true understanding and reasoning capabilities. While acknowledging their impressive ability to mimic human language, the author emphasizes that this mimicry shouldn't be mistaken for genuine intelligence. They believe the focus should shift from scaling existing models to developing new architectures that address the core issues of understanding and reasoning. The current trajectory, in their view, is a dead end that will only lead to more sophisticated mimicry, not actual progress towards artificial general intelligence.

  The author of the Twitter post expresses profound bewilderment at the persistent optimistic outlook some individuals hold regarding Large Language Models (LLMs). They articulate this confusion by highlighting what they perceive as the inherent limitations and unresolved issues plaguing these models. Specifically, they contend that LLMs demonstrate a superficial understanding of language, primarily mimicking patterns and statistical correlations without grasping the underlying meaning or possessing genuine comprehension. This lack of true understanding, they argue, manifests in the models' propensity for generating nonsensical or factually incorrect outputs, a phenomenon often referred to as "hallucination."

  Furthermore, the author underscores the substantial computational resources required to train and operate these models, questioning the scalability and practical applicability of LLMs in the long term, especially given what they see as the limited returns in terms of actual intelligence and problem-solving capabilities. They seem to imply that the current excitement surrounding LLMs might be driven more by hype and inflated expectations than by a realistic assessment of their current capabilities and future potential. The author's skepticism appears rooted in a fundamental disagreement with the premise that scaling up model size and data volume will inevitably lead to genuine intelligence or a meaningful resolution of the inherent limitations they have observed. Essentially, they question the core assumption driving much of the LLM research and development, suggesting a need for a more critical evaluation of the current trajectory of the field.

  • LLMs
  • Large Language Models
  • AI Skepticism
  • artificial intelligence
  • AI Hype
  • Tech Criticism
  • Critique of LLMs
  • Limitations of LLMs
  • AI Ethics
  • AI Safety

  Summary of Comments ( 65 )
  https://news.ycombinator.com/item?id=43498338

  Verbose tl;dr

  Hacker News users discuss the limitations of LLMs, particularly their lack of reasoning abilities and reliance on statistical correlations. Several commenters express skepticism about LLMs achieving true intelligence, arguing that their current capabilities are overhyped. Some suggest that LLMs might be useful tools, but they are far from replacing human intelligence. The discussion also touches upon the potential for misuse and the difficulty in evaluating LLM outputs, highlighting the need for critical thinking when interacting with these models. A few commenters express more optimistic views, suggesting that LLMs could still lead to breakthroughs in specific domains, but even these acknowledge the limitations and potential pitfalls of the current technology.

  The Hacker News post titled "I genuinely don't understand why some people are still bullish about LLMs," referencing a tweet expressing similar sentiment, has generated a robust discussion with a variety of viewpoints. Several commenters offer compelling arguments both for and against continued optimism regarding Large Language Models.

  A significant thread revolves around the distinction between current limitations and future potential. Some argue that the current hype cycle is inflated, and LLMs, in their present state, are not living up to the lofty expectations set for them. They point to issues like lack of true understanding, factual inaccuracies (hallucinations), and the inability to reason logically as core problems that haven't been adequately addressed. These commenters express skepticism about the feasibility of overcoming these hurdles, suggesting that current approaches might be fundamentally flawed.

  Conversely, others maintain a bullish stance by emphasizing the rapid pace of development in the field. They argue that the progress made in just a few years is astonishing and that dismissing LLMs based on current limitations is shortsighted. They draw parallels to other technologies that faced initial skepticism but eventually transformed industries. These commenters highlight the potential for future breakthroughs, suggesting that new architectures, training methods, or integrations with other technologies could address the current shortcomings.

  A recurring theme in the comments is the importance of defining "bullish." Some argue that being bullish doesn't necessarily imply believing LLMs will achieve artificial general intelligence (AGI). Instead, they see significant potential for LLMs to revolutionize specific domains, even with their current limitations. They cite examples like coding assistance, content generation, and data analysis as areas where LLMs are already proving valuable and are likely to become even more so.

  Several commenters delve into the technical aspects, discussing topics such as the limitations of transformer architectures, the need for better grounding in real-world knowledge, and the potential of alternative approaches like neuro-symbolic AI. They also debate the role of data quality and quantity in LLM training, highlighting the challenges of bias and the need for more diverse and representative datasets.

  Finally, some comments address the societal implications of widespread LLM adoption. Concerns are raised about job displacement, the spread of misinformation, and the potential for malicious use. Others argue that these concerns, while valid, should not overshadow the potential benefits and that focusing on responsible development and deployment is crucial.

  In summary, the comments section presents a nuanced and multifaceted perspective on the future of LLMs. While skepticism regarding current capabilities is prevalent, a significant number of commenters remain optimistic about the long-term potential, emphasizing the rapid pace of innovation and the potential for future breakthroughs. The discussion highlights the importance of differentiating between hype and genuine progress, acknowledging current limitations while remaining open to the transformative possibilities of this rapidly evolving technology.

• Coping with dumb LLMs using classic ML

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  Posted: 2025-01-22 09:25:07

  Verbose tl;dr

  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.

  • Large Language Models
  • LLMs
  • Classic Machine Learning
  • ML
  • artificial intelligence
  • AI
  • natural language processing
  • NLP
  • Decision Trees
  • Model Evaluation
  • Limitations of LLMs
  • Hybrid Approaches

  Summary of Comments ( 44 )
  https://news.ycombinator.com/item?id=42790820

  Verbose tl;dr

  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.