Stories with Tag Adversarial Examples

• Your AI Can't See Gorillas

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  Posted: 2025-02-05 16:33:55

  Verbose tl;dr

  Large language models (LLMs) excel at mimicking human language but lack true understanding of the world. The post "Your AI Can't See Gorillas" illustrates this through the "gorilla problem": LLMs fail to identify a gorilla subtly inserted into an image captioning task, demonstrating their reliance on statistical correlations in training data rather than genuine comprehension. This highlights the danger of over-relying on LLMs for tasks requiring real-world understanding, emphasizing the need for more robust evaluation methods beyond benchmarks focused solely on text generation fluency. The example underscores that while impressive, current LLMs are far from achieving genuine intelligence.

  Chiraag Gohel's blog post, "Your AI Can't See Gorillas," delves into the critical yet often overlooked aspect of exploratory data analysis (EDA) when working with large language models (LLMs). The central argument revolves around the inherent limitations of LLMs in fully comprehending the nuances and complexities within datasets, particularly those containing unstructured or semi-structured data like text. Gohel utilizes the metaphor of a gorilla in a dataset, representing an unexpected or anomalous pattern that, while potentially obvious to a human observer conducting thorough EDA, might remain entirely invisible to an LLM.

  He meticulously illustrates this point through several practical examples. He demonstrates how relying solely on aggregate metrics, like average sentiment or topic distribution, can mask underlying issues. A seemingly positive average sentiment, for instance, could conceal a significant subset of highly negative sentiments within the dataset. He further emphasizes the importance of visualizing the data through histograms and scatter plots, techniques that allow for the identification of outliers, unusual distributions, and other irregularities that could indicate data quality problems or reveal hidden insights. These visualizations, Gohel argues, are analogous to a human "seeing" the gorilla, something an LLM, operating primarily on statistical patterns, might miss.

  The post elaborates on the crucial role of human intuition and domain expertise in interpreting the findings from EDA. While LLMs excel at processing vast quantities of data and identifying statistical correlations, they lack the contextual understanding and critical thinking abilities necessary to make sense of these correlations in a meaningful way. Gohel stresses that EDA should not be viewed as a mere preprocessing step but as an iterative and interactive process involving continuous exploration, questioning, and refinement of understanding. This involves going beyond simply calculating summary statistics and diving deeper into the data to uncover hidden patterns and potential biases.

  Furthermore, the post highlights the dangers of deploying LLMs without adequate EDA, warning that this can lead to biased, inaccurate, or even harmful outcomes. By bypassing thorough EDA, developers risk perpetuating existing biases present in the data, leading to models that reinforce these biases and produce unfair or discriminatory results.

  In conclusion, Gohel's "Your AI Can't See Gorillas" serves as a potent reminder of the indispensable role of human-driven EDA in the age of LLMs. It underscores the limitations of relying solely on automated analysis and advocates for a more nuanced and iterative approach that combines the computational power of LLMs with the critical thinking and domain expertise of human analysts. This combined approach, he argues, is essential for developing robust, reliable, and ethically sound AI systems.

  • AI
  • artificial intelligence
  • Large Language Models
  • LLMs
  • computer vision
  • Image Recognition
  • Bias in AI
  • AI Limitations
  • Data Bias
  • Exploratory Data Analysis
  • EDA
  • machine learning
  • Gorilla
  • Adversarial Examples
  • AI Safety

  Summary of Comments ( 119 )
  https://news.ycombinator.com/item?id=42950976

  Verbose tl;dr

  Hacker News users discussed the limitations of LLMs in visual reasoning, specifically referencing the "gorilla" example where models fail to identify a prominent gorilla in an image while focusing on other details. Several commenters pointed out that the issue isn't necessarily "seeing," but rather attention and interpretation. LLMs process information sequentially and lack the holistic view humans have, thus missing the gorilla because their attention is drawn elsewhere. The discussion also touched upon the difference between human and machine perception, and how current LLMs are fundamentally different from biological visual systems. Some expressed skepticism about the author's proposed solutions, suggesting they might be overcomplicated compared to simply prompting the model to look for a gorilla. Others discussed the broader implications of these limitations for safety-critical applications of AI. The lack of common sense reasoning and inability to perform simple sanity checks were highlighted as significant hurdles.

  The Hacker News post "Your AI Can't See Gorillas" (linking to an article about LLMs and Exploratory Data Analysis) has several comments discussing the limitations of LLMs, particularly in tasks requiring visual or spatial reasoning.

  Several commenters point out that the "gorilla" problem isn't specific to AI, but a broader issue of attention and perception. Humans, too, can miss obvious details when their focus is elsewhere, referencing the famous "invisible gorilla" experiment. This suggests the issue is less about the type of intelligence (artificial or biological) and more about the nature of attention itself.

  One commenter suggests the article title is misleading, arguing that the problem lies not in the LLM's inability to "see," but its lack of training on tasks requiring visual analysis and object recognition. They argue that specialized models, like those trained on image data, can "see" gorillas.

  Another commenter highlights the importance of incorporating diverse data sources and modalities into LLMs, moving beyond text to encompass images, videos, and other sensory inputs. This would allow the models to develop a more comprehensive understanding of the world and perform tasks requiring visual or spatial reasoning, like identifying a gorilla in an image.

  The discussion also touches upon the challenges of evaluating LLM performance. One commenter emphasizes that standard metrics may not capture the nuances of complex real-world tasks, and suggests focusing on specific capabilities rather than general intelligence.

  Some commenters delve into the technical aspects of LLMs, discussing the role of attention mechanisms and the potential for future development. They suggest that incorporating external tools and APIs could augment LLM capabilities, enabling them to access and process visual information.

  A few comments express skepticism about the article's premise, arguing that LLMs are simply tools and should not be expected to possess human-like perception or intelligence. They emphasize the importance of understanding the limitations of these models and using them appropriately.

  Finally, there's a brief discussion about the practical implications of these limitations, particularly in fields like data analysis and scientific discovery. Commenters suggest that LLMs can still be valuable tools, but human oversight and critical thinking remain essential.

• Show HN: I Created ErisForge, a Python Library for Abliteration of LLMs

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  Posted: 2025-01-27 15:29:54

  Verbose tl;dr

  ErisForge is a Python library designed to generate adversarial examples aimed at disrupting the performance of large language models (LLMs). It employs various techniques, including prompt injection, jailbreaking, and data poisoning, to create text that causes LLMs to produce unexpected, inaccurate, or undesirable outputs. The goal is to provide tools for security researchers and developers to test the robustness and identify vulnerabilities in LLMs, thereby contributing to the development of more secure and reliable language models.

  A Hacker News user has announced the creation and release of ErisForge, a Python library explicitly designed to disrupt and degrade the performance of Large Language Models (LLMs). The library, available on GitHub, offers a collection of techniques and tools aimed at systematically exploiting vulnerabilities and weaknesses in LLMs, effectively "abliterating" their functionality. This "abliteration" refers to significantly reducing the accuracy, coherence, and overall usefulness of the LLM's output. The stated goal isn't constructive criticism or improvement of LLMs, but rather to demonstrate their inherent fragility and susceptibility to manipulation.

  ErisForge provides various methods to achieve this disruption. These methods can likely include adversarial attacks, specifically crafted prompts designed to confuse or trick the model, and the generation of nonsensical or contradictory text that can poison the LLM’s training data or otherwise interfere with its ability to generate meaningful output. The library likely allows users to experiment with different attack strategies, adjust parameters to fine-tune the disruption techniques, and potentially automate the process of attacking LLMs. The developer frames this project as a means of exposing the limitations and potential dangers of relying on LLMs, emphasizing their vulnerability to malicious exploitation. The implication is that without robust safeguards and a deeper understanding of these vulnerabilities, LLMs could be easily manipulated to produce unreliable or harmful content. The name "ErisForge," invoking the Greek goddess of discord and strife, underscores the destructive and disruptive nature of the library's purpose. The project is open-source, allowing others to contribute to the development of new attack vectors and further explore the vulnerabilities of LLMs.

  • Python
  • LLM
  • Large Language Model
  • AI
  • artificial intelligence
  • machine learning
  • deep learning
  • natural language processing
  • NLP
  • Library
  • Open Source
  • GitHub
  • ErisForge
  • Adversarial Examples
  • Adversarial Attacks
  • Security
  • Robustness
  • AI Safety
  • testing

  Summary of Comments ( 39 )
  https://news.ycombinator.com/item?id=42842123

  Verbose tl;dr

  HN commenters generally expressed skepticism and amusement towards ErisForge. Several pointed out that "abliterating" LLMs is hyperbole, as the library simply generates adversarial prompts. Some questioned the practical implications and long-term effectiveness of such a tool, anticipating that LLM providers would adapt. Others jokingly suggested more dramatic or absurd methods of "abliteration." A few expressed interest in the project, primarily for research or educational purposes, focusing on understanding LLM vulnerabilities. There's also a thread discussing the ethics of such tools and the broader implications of adversarial attacks on AI models.

  The Hacker News post titled "Show HN: I Created ErisForge, a Python Library for Abliteration of LLMs" at https://news.ycombinator.com/item?id=42842123 has generated a moderate number of comments discussing the ErisForge library and its purpose.

  Several commenters express skepticism about the effectiveness of the library in truly "abliterating" LLMs. They point out that the methods used, like prompt injection, are already well-known and that LLM developers are actively working on mitigating these vulnerabilities. One commenter argues that the term "abliteration" is hyperbolic and misrepresents the library's capabilities. They suggest that the library might be more accurately described as a tool for exploring LLM vulnerabilities rather than a weapon for destroying them.

  Some commenters raise ethical concerns about the potential misuse of such a library. They worry that it could be used to generate harmful content or bypass safety measures implemented by LLM providers. The discussion touches upon the responsibility of developers in creating tools that could be used for malicious purposes.

  There's discussion on the actual meaning of "abliteration" in this context. Commenters question whether the goal is to completely disable LLMs, degrade their performance, or simply expose their weaknesses. This leads to a conversation about the different types of attacks that could be used against LLMs and their potential impact.

  A few commenters express interest in the library as a tool for security research and red teaming. They acknowledge the importance of understanding LLM vulnerabilities to develop more robust and secure models. They see the library as a potentially valuable resource for identifying and mitigating these weaknesses.

  Finally, there are some technical comments discussing the specific techniques used by the library and their potential effectiveness. These comments delve into the details of prompt injection and other adversarial attacks, and explore the limitations and potential countermeasures.

  While no single comment is overwhelmingly compelling, the collective discussion provides valuable insights into the potential benefits and risks of ErisForge and similar tools. The conversation highlights the ongoing tension between the rapid advancement of LLM technology and the need for responsible development and mitigation of potential harms.