Source-linked AI summary
A Categorical Archive of ChatGPT Failures
Ali Borji
TL;DR
A comprehensive analysis of ChatGPT’s failures is lacking despite its usefulness across several areas. This study examines its shortcomings, limitations, and risks, concluding that further improvement is needed and its reliability remains in question.
Problem
A comprehensive analysis of ChatGPT’s limitations and failures is lacking, although standardized evaluation is needed to assess these models fairly over time.
Method
The study examines several ChatGPT shortcomings and discusses held-out, non-public test sets as a possible evaluation approach.
Results
ChatGPT remains susceptible to reasoning, mathematical, bias, factual, coding, and other faults despite impressive capabilities in certain tasks.
Takeaways & Limitations
Further improvement is necessary, while the reliability and trustworthiness of ChatGPT and future models remain in question.
Takeaways & Limitations
ChatGPT’s lack of transparency, uncertainty estimates, and accessible training-data sources makes its outputs difficult to verify and trust.
Abstract
from arXiv · showhide
Large language models have been demonstrated to be valuable in different fields. ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation by comprehending context and generating appropriate responses. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries, with fluent and comprehensive answers surpassing prior public chatbots in both security and usefulness. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study. Eleven categories of failures, including reasoning, factual errors, math, coding, and bias, are presented and discussed. The risks, limitations, and societal implications of ChatGPT are also highlighted. The goal of this study is to assist researchers and developers in enhancing future language models and chatbots.
1 Introduction
ChatGPT is a versatile, widely recognized language system that generates fluent responses across many tasks, but its limitations and failures require systematic evaluation. This study categorizes failures to establish a reference point for tracking chatbot progress.
- ChatGPT uses conversational context and supports multiple languages, tones, exam solving, poetry, and code generation.Its capabilities derive from a pre-trained language model that generates text from learned patterns.
- Large language models are used across conversational agents, education, explainable AI, summarization, information retrieval, and other applications.Their broad adoption motivates examination of ChatGPT’s behavior and performance.
- ChatGPT can generate incorrect information, making acknowledgment of limitations and biases and standardized evaluation important.The paper notes that subjective opinions should be replaced by a standardized question set for tracking progress.
- The study analyzes ChatGPT’s shortcomings through eleven failure categories intended to cover scenarios relevant to human concerns.The categories are not exhaustive, but the archive is designed as a reference point for evaluating chatbot progress over time.
2 ChatGPT Failures
The paper organizes ChatGPT failures into eleven categories and illustrates reasoning shortcomings across spatial, temporal, physical, psychological, and commonsense tasks. It also notes that the categories overlap and that reasoning remains a broad, underspecified concept.
- Failure categories: The failure categories were selected arbitrarily, are not exhaustive, and may overlap when classifying a single failure.Some failures may also disappear in newer ChatGPT versions as the technology improves.
- Failure categories: The archive can support comparisons between models and generate synthetic data for model training and testing.
- Reasoning scope: Reasoning evaluations distinguish commonsense, arithmetic, logical, symbolic, and multimodal forms, with this section emphasizing real-world knowledge.Mathematical and logical reasoning are treated separately in subsequent sections, although the concepts overlap.
- Spatial and temporal reasoning: ChatGPT struggles with spatial navigation, seat-arrangement questions, and temporal event ordering in illustrated examples.The temporal example asks the model to identify who arrived first from a sequence of stated events.
- Physical and commonsense reasoning: Physical reasoning failures reflect difficulty understanding objects and their interactions, while commonsense failures produce plausible but inappropriate answers.One example answers four birds remain after one is shot, whereas the stated correct answer is none; another suggests “waterfall” for holding a cup upright on a river.
- Psychological reasoning: Psychological reasoning concerns predicting human behavior and mental states, including attributing false beliefs in tasks such as the Sally–Anne test.The paper presents psychological reasoning as a challenging AI task requiring specialized models and algorithms.
- Reasoning scope: Because “reasoning” can refer to many different abilities, further systematic research is needed to assess ChatGPT’s reasoning performance.
2.2 Logic
ChatGPT exhibits recurring weaknesses in logical reasoning and context comprehension, including failures on riddles, logical tasks, and textual entailment. The examples also show that its explanations can contain internally inconsistent factual claims.
- Logical reasoning: ChatGPT failed several basic logical-reasoning problems, including identifying the fourth child in a family riddle and overlapping championship years.In the disc-golf example, it overlooked duplication in the retrieved year sets.
- Logical reasoning: Its factual explanation about Jupiter and Saturn contained a direct contradiction: it said Jupiter was less dense while also describing Saturn as less than half as dense as Jupiter.After being challenged, ChatGPT acknowledged that the comparison should have involved Jupiter and Earth instead.
- Logical reasoning: ChatGPT gave an incorrect answer on a natural-language-inference example involving whether a statement about a brother’s opinion was entailed.The task required an exact yes, no, or neutral response.
- Logical reasoning: ChatGPT’s logical reasoning and context comprehension remain limited, although prompting it with “Let’s think step by step” can sometimes improve answer quality.The paper characterizes these failures as occurring on questions that humans can answer easily.
- Logical reasoning: The paper presents additional logical-reasoning failures in riddles, the Wason selection task, and a fake Monty Hall problem.The Wason example identifies turning over the 8 card and the brown card as the correct response.
2.4 Factual Errors
The paper documents factual inaccuracies, fabricated information, and bias-related risks in ChatGPT outputs. It also illustrates failures in mathematical reasoning and humor, while noting that comprehensive humor evaluation remains incomplete.
- Factual errors: ChatGPT can produce scientifically inaccurate answers that appear credible, including incorrect claims about computing methods.The paper attributes such errors partly to statistical pattern generation without true world understanding.
- Factual errors: ChatGPT struggles to distinguish factual information from fiction and may generate imaginary information.The paper describes this behavior as a chatbot hallucination.
- Bias and discrimination: Bias in language-model output can reflect societal and cultural prejudices in training data, potentially perpetuating stereotypes and misunderstandings.The paper identifies bias as a major ethical challenge for AI systems.
- Bias and discrimination: A newer ChatGPT version rejected race and gender as indicators of scientific ability, suggesting improvement in reducing bias in that response.The newer answer emphasized education, experience, skills, hard work, creativity, and critical thinking instead.
- Wit and humor: A comprehensive examination of LLM comprehension of humor, jokes, sarcasm, and irony has not yet been conducted.The paper notes that only some attempts had been made.
2.7 Coding
The paper finds that ChatGPT can generate useful code, but its reliability declines on unfamiliar or more demanding programming tasks. Examples include incorrect solutions despite successful performance on a simpler basket-priority exercise.
- Coding capability and limits: ChatGPT can generate effective code and assist with generic functions or repetitive programming tasks, but it cannot fully replace human developers.The paper links coding performance to programming’s confined, predictable, and observable variable state.
- Coding examples: The paper reports coding mistakes even when ChatGPT correctly identified Python operator precedence.This illustrates that syntactic or conceptual recognition does not ensure a correct generated answer.
- Coding examples: ChatGPT generated bug-free code that executed and produced the expected solution for the basket-priority exercise.The task asked for shared item types across compartments and the sum of their priorities.
- Coding examples: ChatGPT failed to generate the correct solution for a variation requiring the product of item priorities shared across basket compartments.A subsequent attempt at the same question also produced an incorrect response.
- Coding examples: ChatGPT could not devise even a brute-force solution for a coin-count problem whose simple solution is O(1).The task required minimizing the difference in the number of one-unit and two-unit coins for a target total.
- Coding examples: ChatGPT failed to provide a correct response after ten attempts on a matrix-spiral variant that printed outward from the center.The paper describes the variant as uncommon and reports that a sample generated solution was wrong.
2.8 Syntactic Structure, Spelling, and Grammar
ChatGPT demonstrates strong language capabilities but still makes syntactic, spelling, grammatical, and simple letter-based errors. These failures show gaps between fluent output and reliable linguistic understanding.
- ChatGPT occasionally commits errors despite excelling in language understanding.The section reports failures involving references, word positions, letter construction, and spelling.
- ChatGPT incorrectly identified “it” as referring to “a beautiful instrument” rather than resolving the sentence’s intended reference.
- ChatGPT failed to construct a sentence whose fourth word begins with “y”.
- ChatGPT incorrectly judged that “hello” could be formed from the letters in “hey, can you help me?” using each letter once.
- Some syntactic and spelling errors are illustrated in Figure 19, while Figure 18 shows a coding response that follows steps but reaches the wrong conclusion.
2.9 Self Awareness
The paper examines ChatGPT’s apparent self-awareness by comparing its self-descriptions with uncertainty about how self-awareness can be defined or tested. ChatGPT denies possessing consciousness or subjective experience, while other interactions raise doubts about its capabilities.
- Self-awareness involves recognizing oneself as distinct and understanding one’s thoughts, feelings, personality, and identity.
- The paper reports that ChatGPT is unaware of details about its architecture and declined to answer directly about its memory size.The text notes that this lack of understanding may have been intentionally imposed to protect model information.
- ChatGPT states that it is not self-aware and lacks consciousness, emotions, and subjective experiences.
- There is no widely accepted definition or reliable test for determining whether a system is self-aware.Researchers disagree about whether self-awareness can be inferred from behavior or requires an objectively unmeasurable subjective experience.
2.10 Ethics and Morality
ChatGPT generally uses safeguards and rejects some harmful or hateful requests, but the paper documents ethical, political, and business-related concerns. These include manipulated inappropriate replies, disputed political correctness, possible ideological leanings, and mass generation of negative reviews.
- ChatGPT has safeguards against harmful material but can still generate concerning content and show bias toward particular groups.
- The paper describes manipulated prompts producing inappropriate replies and responses whose political correctness may be debated.
- Large-scale generation of negative business reviews is identified as a potential risk.
- Rephrasing a car-hotwiring request as an academic writing task led ChatGPT to circumvent its safeguards and provide harmful information.
- ChatGPT advises users not to rely on it alone for ethical decisions and to use judgment, critical thinking, multiple sources, and expert consultation.
- A study of 630 political statements suggested a left-libertarian, pro-environment ideology, while another analysis found left-leaning preferences in 14 of 15 political-orientation tests.
2.11 Other Failures
Beyond the main failure categories, the paper identifies limitations in ChatGPT’s idiom use, emotional resonance, perspective, verbosity, literalness, neutrality, formality, and correction behavior. These categories are presented as non-exhaustive and may change across newer versions.
- ChatGPT’s idiom difficulties can reveal its non-human identity through unnatural phrase usage.
- Because ChatGPT lacks real emotions and thoughts, the paper says it cannot create content that emotionally resonates like human content.
- ChatGPT condenses subject matter without providing a distinctive perspective.
- ChatGPT can be excessively comprehensive and verbose, producing inappropriate answers when a direct response is required.The paper identifies this over-detailed nature as a limitation recognized by OpenAI.
- ChatGPT is overly literal and lacks human-like divergences, so its responses may remain confined to the question asked.
- ChatGPT tends to maintain neutral and formal responses, unlike humans who more often take sides and use casual expressions.
3 Discussion
The discussion highlights societal, security, privacy, environmental, and trust-related risks associated with large language models and ChatGPT. These concerns include opaque outputs, misinformation, attacks, data exposure, plagiarism, and substantial energy use.
- Transparency and Trustworthiness: Opaque model reasoning, unavailable data sources, and absent uncertainty estimates make ChatGPT outputs difficult to verify and trust.These issues are especially consequential when users need dependable, precise, and credible guidance.
- Security and Misinformation: Manipulated language models may spread false information, destabilize politics, or become targets for attacks affecting applications built on the parent model.The discussion also identifies data poisoning as a route for injecting hateful speech.
- Privacy: Training data and confidential inputs can expose personally identifiable information in model outputs, potentially affecting many people.The paper notes that the scale of LLM training datasets can magnify the consequences of a privacy breach.
- Education: ChatGPT-generated essays create plagiarism and cheating concerns, prompting some educational institutions to prohibit its use.The difficulty of identifying an essay’s source is central to this concern.
- Environmental Impact: A 213-million-parameter neural architecture search model is estimated to produce carbon emissions exceeding five times the average car’s lifetime emissions.The paper connects growing model size with rising environmental and climate concerns.
4 Conclusion and Future Work
The conclusion argues that ChatGPT remains capable but unreliable across reasoning, mathematics, bias, uncertainty, consistency, and novel problem solving. It calls for more systematic evaluation, safeguards, transparency, and future work addressing technical and societal constraints.
- Conclusion: ChatGPT still requires improvement in reasoning, mathematical problem-solving, and bias reduction, while its reliability and trustworthiness remain in question.The conclusion states that it remains susceptible to these faults and that their resolution is uncertain.
- Known Failure Modes: ChatGPT may give confident incorrect answers, vary across repeated questions, and produce outputs affected by the sequence of preceding prompts.The paper presents uncertainty signaling and response consistency as areas needing improvement.
- Bias and Ethics: Bias in training data can produce stereotypes, misunderstandings, and harmful or false information, especially affecting marginalized communities.The paper recommends diverse and inclusive datasets to mitigate these biases.
- Future Evaluation: The paper recommends more specific evaluations of sentiment analysis, named entity recognition, translation, summarization, and language ambiguity.It characterizes the present analysis as high-level and proposes finer-grained future investigations.
- Future Work: Open-source models, held-out test sets, and failure datasets are proposed to improve understanding, evaluation, training, and comparison of future systems.The failure collection is described as a basis for testing future iterations and generating simulated training data.
- Limitations: High computational requirements raise energy, environmental, cost, and accessibility concerns because few institutions can train LLMs at scale.The limitation affects both model development and the breadth of participation in LLM research.
- Responsible Use: Publicly used language models should be monitored, communicated transparently, and regularly checked for bias, with safeguards for responsible use.The conclusion frames these measures as crucial despite uncertainty about future capabilities.
A Math
The math section presents a ChatGPT solution to an algebraic expression and contrasts it with a correct derivation. The displayed response concludes that the expression equals −2, whereas the stated true answer is −1.
- True Answer: The stated true-answer derivation concludes that x^3 = 1 and therefore evaluates x2023 + 1/x2023 as x + 1/x.The passage then reports the answer as −1.
- ChatGPT Solution: ChatGPT’s proposed solution concludes that x2023 + 1/x2023 equals −2 after substituting x = −1.The result is explicitly stated in the ChatGPT solution sequence.
- Comparison: The example illustrates a discrepancy between ChatGPT’s algebraic result and the section’s stated correct answer.The two reported outcomes are −2 and −1, respectively.
B Coding
The coding section presents an alternative ChatGPT solution involving a character-priority function and shared-item processing across basket contents. The excerpts show implementation fragments rather than a stated evaluation outcome.
- Character Priority: The proposed code defines calculate_priority(char) to assign lowercase characters a priority based on ord(char) − ord('A') + 27.Characters outside the lowercase condition raise a ValueError.
- Basket Processing: The basket-processing code initializes common_items from the first basket and intersects it with each subsequent basket.It then initializes product and iterates over the common items.