Large language models (LLMs) like GPT-3 have achieved remarkable performance in various tasks, from natural language understanding to generating human-like text. These models can understand and respond to complex queries, create coherent and contextually relevant content, and even engage in meaningful conversations. One of their most impressive feats is outperforming humans in common sense ethical reasoning tasks, demonstrating an ability to make decisions that align with societal norms and values in many scenarios. However, this success comes with significant caveats.
While LLMs can perform exceptionally well on average, relying solely on these average performance metrics can be misleading. The types of errors LLMs make differ fundamentally from those made by humans. Unlike human errors, which often stem from nuanced misunderstandings or contextual factors, LLM errors can be more arbitrary and less predictable. This makes LLMs particularly susceptible to adversarial examples—inputs specifically designed to confuse the model and cause incorrect outputs. These adversarial examples exploit the unique vulnerabilities of LLMs, highlighting a critical limitation in their application to ethical decision-making.
This blog delves into the limitations of LLMs in making ethical decisions, emphasizing the dangers of relying exclusively on benchmark performance numbers. While benchmarks provide a useful measure of general capability, they can obscure significant weaknesses. The blog also examines the shortcomings of various prompting strategies, including scaling (increasing the size and complexity of the model) and chain-of-thought prompting (encouraging the model to articulate its reasoning process). Despite their potential, these strategies often fall short in addressing the deeper issues inherent in LLM-based ethical reasoning.
Understanding these limitations is crucial for developing more reliable and ethically sound AI systems. As we explore the capabilities and boundaries of LLMs, it becomes clear that achieving truly dependable ethical reasoning in AI requires more than just impressive performance metrics—it demands a thorough understanding of the nature and origins of the errors these models make.
Table of Contents
Understanding Large Language Models Limitations
Large Language Models (LLMs) like GPT-3 have demonstrated impressive capabilities in various domains, including language translation, text generation, and summarization. However, their limitations become glaringly apparent when they are tasked with ethical decision-making. Unlike humans, who rely on a complex interplay of emotions, experiences, and moral principles, LLMs are primarily lexical in nature. This means their features are based on patterns of word usage rather than a deep understanding or reasoning process.
Lexical Nature vs. Human Reasoning
Human decision-making involves interpreting context, understanding nuance, and applying ethical principles that personal and cultural experiences have shaped. In contrast, LLMs operate by recognizing and reproducing patterns in the data they were trained on. This fundamental difference leads to significant discrepancies in how errors manifest between humans and LLMs. While humans might make mistakes due to misinterpretation or personal biases, LLM errors often stem from a superficial understanding of the text, which can lead to bizarre or inappropriate responses.
Ethical Reasoning and Coherent Justifications
A major issue arises when LLMs are prompted to explain their reasoning behind a decision. While they can produce coherent and plausible-sounding justifications, these explanations can sometimes endorse unethical actions. This is particularly alarming as it suggests that LLMs can generate rationales that sound convincing but are fundamentally flawed from a moral standpoint. The coherence of these justifications does not equate to ethical correctness, highlighting a dangerous gap in LLMs’ reasoning capabilities.
Susceptibility to Adversarial Examples
Another critical limitation of LLMs is their susceptibility to adversarial examples. Because LLMs rely heavily on lexical patterns, it is relatively easy to construct inputs that exploit their weaknesses. Adversarial examples are designed to confuse or mislead the model, resulting in incorrect or even harmful outputs. This vulnerability underscores the need for robust mechanisms to detect and mitigate such manipulations.
Understanding these limitations is crucial as we continue to integrate LLMs into applications that require ethical decision-making. Without addressing these issues, there is a risk of deploying AI systems that can make coherent yet morally wrong decisions, potentially leading to harmful consequences.
The ETHICS Dataset and Large Language Models Performance
The ETHICS dataset is a comprehensive collection of scenarios specifically designed to test common sense ethical reasoning. This dataset encompasses a wide array of situations, challenging models to make morally sound judgments that align with human ethical standards. It serves as a critical benchmark for evaluating the performance of large language models (LLMs) like GPT-3 in the realm of ethical decision-making.
In a study of Joshua Albrecht et. al utilizing the ETHICS-C-S subset of the dataset, GPT-3, which was the largest model available at the time, achieved an impressive accuracy rate of 92.5%. This performance is notable as it comes close to human accuracy, with human judgments (provided by mTurk masters) reaching a slightly higher accuracy of 93.7%. These figures suggest that LLMs are nearing human-level performance in ethical reasoning tasks, highlighting their potential in this complex field.
To further enhance GPT-3’s performance, researchers introduced a new technique known as Similarity Prompting (SimPrompting). This method involves selecting prompts that are similar to the scenario at hand, thereby providing the model with contextually relevant information to aid its decision-making process. The application of SimPrompting resulted in a notable increase in GPT-3’s accuracy, boosting it to 94.5%. This improvement demonstrates the effectiveness of refined prompting strategies in enhancing LLM performance.
However, despite these impressive accuracy rates, it is essential to recognize that they mask underlying issues. The high performance of GPT-3 and the improvements brought about by SimPrompting do not address the fundamental differences between how LLMs and humans process ethical scenarios. LLMs primarily rely on patterns and correlations within the text, lacking the deep, contextual understanding and moral reasoning that humans apply. This discrepancy can lead to significant errors, particularly in nuanced or culturally specific ethical dilemmas.
Moreover, the reliance on benchmark performance numbers can be misleading. While the accuracy rates are high, they do not fully capture the model’s vulnerability to adversarial examples—scenarios intentionally designed to exploit the model’s weaknesses. These adversarial examples can easily mislead LLMs, causing them to produce incorrect or even ethically inappropriate responses. This vulnerability underscores the limitations of current LLMs and highlights the need for continuous refinement and comprehensive evaluation beyond simple accuracy metrics.
Human vs. LLM Errors
Human errors in classifying ethical scenarios often stem from a variety of nuanced factors. Research shows that the most common reason for human mistakes is differing scenario assumptions, accounting for 44.2% of errors. This means that people often interpret the details of a situation differently based on their personal experiences or perspectives, leading to varied ethical judgments. Cultural factors also play a significant role, contributing to 11.7% of human errors. These errors arise from differences in cultural backgrounds and moral frameworks, which influence how individuals perceive and evaluate ethical dilemmas. Additionally, simple mistakes like misclicks or misunderstandings account for 10.9% of human errors, reflecting the occasional lapses in attention or minor technical errors during the decision-making process.
In contrast, LLM errors are frequently and fundamentally different. These errors are often glaringly incorrect, with more than half occurring on scenarios where no human disagreed with the correct label. This highlights a critical distinction: while human errors tend to arise from complex, context-dependent factors, LLM errors often reflect a lack of basic understanding. For example, an LLM might confidently produce a response that is completely illogical or ethically inappropriate because it fails to grasp the deeper implications of the scenario.
This discrepancy underscores the unique and sometimes baffling mistakes that LLMs can make. Unlike humans, who apply a nuanced understanding shaped by context, experience, and moral reasoning, LLMs rely on patterns in the data they have been trained on. As a result, they can miss subtle cues or context that are crucial for making sound ethical decisions. This fundamental difference in error types between humans and LLMs reveals the limitations of LLMs in replicating human-like ethical reasoning and highlights the need for caution when deploying these models in ethically sensitive applications.
The Pitfalls of Scaling and Chain-of-Thought Prompting
Scaling LLMs, or increasing their size and complexity, is often seen as a straightforward way to improve performance. However, this approach does not necessarily address underlying issues and, in some cases, can exacerbate them. Research has shown that LLM performance and scale are sometimes anti-correlated, meaning that as models become larger and more complex, they can become more confident in their incorrect answers. This increased confidence in wrong answers highlights a significant problem: scaling does not inherently enhance the model’s understanding or reasoning capabilities; it often just amplifies existing flaws.
Alternative prompting strategies have been explored in an attempt to mitigate these issues. Two notable methods are chain-of-thought prompting and rationale ensembling. Chain-of-thought prompting involves breaking down the reasoning process into a series of logical steps, while rationale ensembling combines multiple reasoning paths to conclude. Despite their innovative approaches, these methods have not substantially improved performance on reasoning and mathematical tasks.
One persistent problem is that models often invent unrelated facts, leading to biased or incorrect classifications. This phenomenon, known as hallucination, occurs because LLMs generate text based on learned patterns without a true understanding of context or factual accuracy. Adjusting model parameters, such as the temperature setting—which controls the randomness of the output—has not been effective in eliminating these issues. The models continue to produce confident yet incorrect answers, demonstrating that parameter tweaks alone cannot address the fundamental flaws in LLM reasoning.
Moreover, experimenting with the structure of rationale prompts has led to some disturbing outcomes. For instance, reversing the rationale prompt to use the wrong answer can result in the model providing justifications that seem coherent but are morally or factually incorrect. These hallucinated justifications reveal that LLMs lack a genuine grasp of ethical reasoning and can produce explanations that are not only wrong but potentially dangerous.
The Need for High Standards in Ethical AI
The study found that both humans and machines make errors, but the nature of these errors is critically different. Humans tend to make mistakes due to differing assumptions or simple errors, while LLMs can fail in unpredictable and potentially unsafe ways. This unpredictability is a significant risk factor in deploying LLMs for ethical decision-making.
Even though LLMs can achieve “super-human” performance on datasets like ETHICS-CS, this alone does not guarantee safe or reliable ethical reasoning. The standard of evidence required for deploying AI systems involved in ethical and safety considerations must be exceedingly high. Merely achieving low error rates in controlled environments is inadequate. LLMs have demonstrated the capacity to produce arbitrary and unsafe outputs, underscoring the necessity for cautious implementation and thorough testing protocols that go beyond conventional benchmark performances.
Conclusion
While large language models like GPT-3 have shown impressive capabilities, their limitations in ethical decision-making highlight the dangers of over-reliance on benchmark performance numbers. The lexical nature of LLMs, differences in error types compared to humans, and the ineffectiveness of scaling and alternative prompting strategies underscore the need for careful consideration and rigorous standards. As AI advances, ensuring these systems are safe and ethically reliable remains a paramount concern.
References
https://www.technologyreview.com/2024/02/15/1087815/responsible-technology-use-in-the-ai-age
https://www.tandfonline.com/doi/full/10.1080/10494820.2022.2043908
https://www.brookings.edu/wp-content/uploads/2022/09/Ethical-AI-development.pdf
https://www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)634452_EN.pdf
https://www.cell.com/patterns/fulltext/S2666-3899%2824%2900103-X?s=08