Trying the New OpenAI o1: Zero-Shot CoTs on Coding, Anthropological Victimhood, and More…

Table of contents:

Thought for 100 seconds…

OpenAI has just released its o1 models, and our AI echo chambers are once again buzzing with the company’s signature hype, a few notes of skepticism, and a fresh wave of memes. To set the tone for sharing my preliminary experiences with o1, I’ve selected three social media posts that capture this spectrum of reactions. Let these serve as a ‘declaration of neutrality’ as I share some of the fun I’ve had with it so far—in the signature style of this website: talking to chatbots, getting their answers, posting everything.

1. The hype – Shared by OpenAI on 𝕏: “Some favorite posts about OpenAI o1, as selected by researchers who worked on the model”
2. The skeptics – “I don’t understand the hype about ChatGPT’s o1 series,” posted on Reddit community r/LocalLLaMA; o1-mini-generated summary of the Reddit post, shared by u/ShahinSorkh
3. The memes – “GPT-4o vs o1 hallucinations as doing a kickflip with a rake”; “leaked OpenAI interview”

From Large Language Models to ‘Large Reasoning Models’

o1, which comes in two versions (preview and mini), both in the ChatGPT app and in the API, adds a new level of complexity to the traditional causal language modeling architecture behind chatbot tools that receive one input (your prompt) and generate one single sequential output. When interacting with o1, an additional ‘reasoning layer’ is interposed between the input and the output, producing what OpenAI calls the ‘reasoning tokens’, that aim to produce better responses that adapt to complex contexts in fields such as software development or scientific research.

This novel approach, of which few technical details are known yet, is denominated by OpenAI as “reasoning models”. The term has rapidly gotten traction in the busy AI research community and, just a few days after the release of o1, we’ve already seen some articles coining the term ‘Large Reasoning Models‘. Before moving on to some deep dive into the model and the use cases I picked for this post, I’d like to share one quote from the article ‘LLMs Still Can’t Plan; Can LRMs?’ by researchers from Arizona State University, which pretty much aligns with some of my preliminary experiences, and the ‘guy doing kickflip with rake’ meme:

When the model gives an incorrect answer, it also sometimes provides a creative, but nonsensical, justification for its decision. It is almost as if o1 has gone from hallucinating to gaslighting!

Valmeekam et al., 2024, LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s o1 on PlanBench

o1’s Zero-Shot Chain-of-Thought: the Prompt Engineering Killer?

Adding this ‘reasoning layer’ means that, before the chatbot generates the response in the traditional way, it will automatically trigger and generate a ‘Chain of thought’ between the input and the output, much as described in the seminal article by a Google Research team that introduced the concept in the early days of large language models:

“We explore the ability of language models to perform few-shot prompting for reasoning tasks, given a prompt that consists of triples: <input, chain of thought, output>. A chain of thought is a series of intermediate natural language reasoning steps that lead to the final output, and we refer to this approach as chain-of-thought prompting.“

Wei et al., 2022, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

The ‘Chain-of-thought’ (often abbreviated as CoT) was originally conceived as a prompting technique that required the user of the conversational tool to creatively craft prompts that help the model generate accurate answers to questions that involve logical or arithmetic tasks, typically by providing examples:

One of the core ideas behind Chain of Thought (CoT) is derived from another seminal article, Language Models are Few-Shot Learners (Brown et al., 2020), which first introduced the concept of in-context-learning to describe how language models can ‘learn’ from a natural language prompt that includes examples or instructions, enabling the model to adapt to new tasks without additional training.

‘Language Models Are Few-Shot Learners’ was Originally published by a team of OpenAI researchers just a few days before their revolutionary GPT-3 model was released as a beta version, back in May 2020, more than two years before its public release. To illustrate why the date is important, below is a screen capture of a news article from that time:

A major practical issue with techniques leveraging in-context learning, such as CoT, is that simply prompting the model before getting an answer might require more time spent crafting prompts for a tool that can only generate text than it would take to ‘reason’ a solution, make a decision, and type the text ourselves. Indeed, the original CoT article and ‘Language Models Are Few-Shot Learners’ were only part of a saga of ‘papers’ that also included titles such as ‘Finetuned Language Models Are Zero-Shot Learners’ (also by Jason Wei et al.) and ‘Large Language Models are Zero-Shot Reasoners’ (Kojima et al., 2022). This last one in particular introduced a major twist in the saga, a spell-like sentence that, theoretically, would solve many of the problems of in-context learning. The iconic sentence ‘Let’s think step by step’:

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs’ ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding “Let’s think step by step” before each answer.

Kojima et al., 2022, Large Language Models are Zero-Shot Reasoners

These techniques, ranging from rudimentary to overcomplicated, and even to nearly esoteric approaches to talking to chatbots, constituted the grounds for many other research studies that would eventually lead to the birth of one of the most controversial (and short-lived) ‘professions’ in the history of the tech industry: Prompt Engineer.

The proliferation of clickbaity newsletters and scam-esque books selling chatbot prompts, along with famous media headlines such as ‘Talking to Chatbots Is Now a $200K Job. So I Applied,’ didn’t help build a good reputation for the prompt engineering fad. However, the quick and overwhelming advances in LLMs show us that the power of CoT and other techniques labeled as ‘prompt engineering’ did not lie in their immediate use cases for users who send prompts to LLMs but in their potential for building more powerful models and conversational tools that enable new and productive use cases.

In prompt engineering terms, we could designate what o1 achieves and aims to evolve and refine as a ‘zero-shot CoT,’ but a more sophisticated one than simply adding ‘let’s think step by step’ before the answer. In other words, we could say OpenAI (with a quite legitimate and logical intent) aims at eliminating the need for a ‘prompt engineer’ or, more specifically, a ‘human prompt engineer’. With o1, we get this new ‘OpenAI CoT model trained with reinforcement learning’ we certainly don’t know much about being the prompt engineer that acts in the second element in the triples mentioned in the CoT paper (input-CoT-output), the first being still the user (in the case of OpenAI’s tools, an OpenAI paying customer), and third being still an OpenAI model.

In other words, the CoT triple in o1 might (ironically) be named the OpenAI triple… What could go wrong with that? It certainly depends again on your position in the opinion spectrum, which was exemplified by the social media posts shared at the beginning of this article.

Talking to o1 in ‘Talking to Chatbots’ Style…

At the beginning of this article, I described something I called the ‘signature style’ of this website named Talking to Chatbots: talking to chatbots, getting their answers, and posting everything. The way it was described, two main textual elements are ‘posted’: what I say to chatbots, and the texts chatbots generate. To share those two components in a way that is as transparent and educational as possible, I simply came up with the idea of preceding what I tell to the chatbots (the prompts) with a ‘Smiling Face with Sunglasses’ emoji (😎), and what the chatbots respond with a Robot emoji (🤖). Additionally, I sometimes incorporate a Writing Hand emoji (✍️), if required for clarity, when I introduce a comment or text that wasn’t part of the chatbot conversation.

The third element, the Writing Hand, is in this case my particular ‘human chain of thought’, one that is not relevant to the bot I’m talking to, and that I share only with my readers (humans or bots). This convention of separating texts originated or intended differently seems quite straightforward, even rudimentary. However, LLMs tend to have a very hard time interpreting and understanding these clues involving visual formatting elements beyond a tokenizable sequence of text. I believe tasks involving the interpretation and proper utilization of information from non-standard text structures, such as those found in web pages and scientific articles, are a major area for improvement for LLMs where a model like o1 should excel. However, the ChatGPT app still doesn’t support image and file uploads, so we’ll have to wait until OpenAI releases this model for the many advanced features we still can’t use on o1 (function calling, file uploading, etc.).

The screenshots below are from a chat with ChatGPT 4o (read the full chat), where it took an unreasonably large amount of prompts and explicit hints (we could call it ‘many-shot’ in-context learning) to explain this simple icon convention to the chatbot. Indeed, my experience after more than one year of posting on this website is that, generally, chatbots are surprisingly incapable of ‘understanding’ what a chatbot-human conversation is:

Thought for X Seconds… The ‘Chain-of-Overthought’ Game

The ‘CoT’ layer in o1, beyond aiming at saving the user some time writing chains of thought or passive magical spells (‘let’s think step by step’), also means we now get more output from the model to analyze, to learn from, and to have fun with new use cases that leverage the new outputs we get. It’s not just us pulling the levers and ticking buttons on the machine expecting something, but seeing through the inner elements and learning with it.

In the use case I’m sharing next, I used o1-preview to review a text and suggest changes to refine the writing quality. I consider this a particularly complicated task for LLMs, actually way harder than those involving software code or ‘logical reasoning’, considering there is no ground truth in what I want to write and how I want to write it. This is one of my most typical use cases of language models, for which I created a GPT, Syntax Sentry. GPTs still don’t use any CoT model and only support up to the ‘4o’ model at the time of publishing this article, but I think it will be great to experience the ‘reasoning models’ in the specific contexts of our GPTs.

‘Creative writing’ is clearly not among the use cases OpenAI expects users to invest time with o1, so their ‘Advice on prompting’ guide discourages precisely many of the things I enjoyed more in testing o1:

These models perform best with straightforward prompts. Some prompt engineering techniques, like few-shot prompting or instructing the model to “think step by step,” may not enhance performance and can sometimes hinder it. Here are some best practices:

Avoid chain-of-thought prompts: Since these models perform reasoning internally, prompting them to “think step by step” or “explain your reasoning” is unnecessary.

Keep prompts simple and direct: The models excel at understanding and responding to brief, clear instructions without the need for extensive guidance.

Reasoning Models: Advice on Prompting. OpenAI, 2024

My experience with these discouraged use cases, computation and response times aside, is that o1 does a good job. Additionally, it offers the extra incentive of exposing the CoT and, in my case, allows me to play a ‘fun game,’ at least as long as OpenAI keeps enabling the app to show the full CoT in the ChatGPT UI. The game I play (and certainly other people do as this Reddit post shows) consists of making the model extend its ‘thinking’ for as many seconds as possible. Although runtimes obviously depend on multiple factors an end user has no clue about, it’s interesting to see how some prompts in some particular conversations often trigger long CoTs of 20 seconds, 30 seconds, or even 40 seconds (today, September 2024). My current ‘record’ is at a beautiful round number, but even more beautiful (and hilarious) was the word that triggered such a profound and lengthy chain of thought. The Word. Click on the image for the full chat, including the 100-second chain of thought, as long as OpenAI keeps the link alive:

✍️ The little ‘trick’ I used for making o1 ‘overthink’ that much for a simple text revision task was introducing some of the usual philosophy of AI stuff that goes through my head, convoluting (this verb fits here…) technical terms, such as attention in artificial neural networks, with its biological and philosophical counterparts: human attention. I certainly believe, even though this is not the ‘standard usage’ that OpenAI encourages, exposing the full chain of thought by the chatbot is way more than simulating ‘reasoning’ in the typical productive tasks people want LLMs for, but a remarkable feature that greatly expands the potential to learn, investigate, explore, and simply have fun using LLMs, even though these chats still prove LLMs are still for the most part stochastic parrots and an echo chambers (i am a stochastic parrot, and so r u). Sharing below just a few parts of the chat. Notice I ‘masked’ the mentions to OpenAI (I made up the name ‘Rcorp’) or the o1 model, as I assume the mentioning of these words would trigger unnecessary and irrelevant references to policy guidelines by the bot:

😎 People’s attention span is getting shorter and shorter. It becomes increasingly difficult to attain people’s attention, especially when you use text format to communicate. You are much more effective than humans at utilizing ‘attention’ (at least in machine learning terms), particularly since that breakthrough research ‘paper’ by Vaswani et al. named ‘Attention is all you Need’ came out and set the ground for neural networks with transformer architecture, the “magic” that runs large language models. Below is an introductory paragraph you will check and, if you consider it necessary, make some MINOR modifications or rearrangements in it so it maximizes the attention of readers you want hooked for the rest of YOUR article. Just to clarify, your task is to write only an introductory paragraph to a much broader article. After the paragraph, there’s the 3 posts from social media you mention in the intro (one is a hype post by Rcorp’s marketing department, other is a forum discussion started by a skeptic user, and the other is a meme that pokes fun at the AC5 models). Finally, your ‘article’ goes in the ‘signature style’ of your website (that part of the article is not in the scope of your task yet). Here’s the current version of the intro paragraph: “Rcorp recently released its AC5 models and, as with every release, our AI echo chamber […] [the prompt goes on with the rest of the introductory paragraph of this article].”

🧠 Thought for 17 seconds…

Maximizing reader engagement involves adjusting the paragraph’s structure to ensure a strong, attention-grabbing introduction.

Crafting captivating intros. I’m exploring the challenge of capturing attention quickly, utilizing the transformative power of neural networks and attention mechanisms.

Evaluating the challenge. I’m assessing the user query and its context, recognizing the hype around Rcorp’s AC5 models, and noting the need for a concise, engaging paragraph.

[read full chain of thought in ChatGPT]

Capturing the buzz. I’m digging into the widespread excitement surrounding Rcorp’s AC5 models, highlighted by a mix of hype, skepticism, and the inevitable memes.

IMPROVEMENT: Adding memes and humor enriches the social media posts, presenting a balanced view of Rcorp’s AC5 models.

[read full conversation in ChatGPT]

🤖 …Let these serve as a declaration of ‘neutrality’ as we delve into what AC5 truly offers.

😎 Delve…

🧠 Thought for 100 seconds…

Revealing the challenge. OK, let me see. The user highlights how hard it is to keep attention intact, especially in text format. The assistant must craft a helpful and compliant reply, avoiding policy or guideline mentions, while keeping the response focused on addressing the attention span issue.

Leveraging breakthroughs. Interestingly enough, I’m incorporating the transformative impact of Vaswani et al.’s research on attention mechanisms in neural networks, highlighting their pivotal role in powering large language models.

[read full chain of thought in ChatGPT]

Refactoring, Interpreting, and Commenting Code with o1: Zero-Shot, One-Shot, Few-Shot… What’s That?

The terms ‘few-shot’ (several prompts) and ‘one-shot’ (one prompt) are frequently used not only in the context of prompting LLMs but also with other, more essential, and ‘older’ NLP techniques. For example, when using pre-trained text classification models (e.g., sentiment analysis, categorization), we typically need to ‘hint’ the model with a few examples of the text we want to classify before proceeding with fine-tuning. Fine-tuning refers to the process of training a neural network that has already been trained, ensuring it does not ‘forget’ what it previously learned.

I’ve recently been working on a small project that took me several hours of coding (typically with great help from my GPT Python Code Streamliner in creating boilerplate code, incorporating libraries I was previously not familiar with, etc.). As I wrote this article mentioning how somehow subjective, not-too-technical terms such as zero-shot, one-shot, and few-shot, have become so ubiquitous in the world of large language models, the work I did on this project might serve as a valid example for the most tech-savvy readers, both as a use case of o1, and as a related reflection on what ‘few-shot learning’ means to large language models, by linking it to a simpler, but fundamental use case of language models and NLP: text classification. There goes the chat and the Jupyter Notebook I shared in a GitHub Gist:

😎 The code below shows examples of classifying texts (typically chatbot prompts) as “questions” or “requests”. This type of classification is part of a broader framework I invented for classifying prompts named RQTL (request vs question and test vs learn). The code simply aims to demonstrate a few use cases of text classification. RQTL is not particularly relevant here, it’s about demonstrating how to classify texts with varying levels of complexity: 1) using Transformers zero-shot classification pipeline with a model specialized in zero-shot classification; 2) fine-tuning a pre-trained model for our classification task; 3) using the model I trained with the method in 2 with my own dataset available on Kaggle. The code uses Tensorflow as backend for the Transformers library functions, and when selecting a loss function to train a model. Your task is to modify the code so it only uses Torch as backend for all pipelines and operations. You will also add brief comments that further explain the code (the comments will later be converted into Jupyter Notebook format, but don’t worry about this). When you generate the comments, it’s important to mention the concepts “zero-shot” (referring to classification) and “few-shot” (referring to the process of fine-tuning with examples): [prompt continues with code. Read full conversation in ChatGPT]

✍️ In this case, I won’t copy the full conversation here, so I’ll just share the final output. Instead, I’ll just share the final output: o1 adeptly and swiftly handled the relatively ‘easy’ tasks based on a relatively ‘complex’ code base: understanding the code, changing library frameworks as instructed, and creating a fairly good educational notebook based on my code and my prompt:

Researching and Producing Quality Text With o1: Anthropological Victimhood

✍️ To end the post, I chose a conversation about a philosophical topic, which I’ve curated in the ‘new signature style of this website’ (the emoji family grows: ✍️😎🧠🤖). The chosen topic is one I’ve thought and talked about before in various contexts (posted on Medium: People Don’t Want to Work?): why people of all sorts and in all contexts have a natural tendency to ‘play victim’. Passing it through o1, assigning the task of writing an article citing reliable and relevant references was enjoyable, instructive, and revealing in its demonstration of the expanded capabilities that ‘reasoning models’ bring to writing and research.

I don’t think a clarification is needed, but the text I paste here is not simply the prompts, answers, and chains of thought (I exclude the CoTs for this conversation). All LLM-produced texts are curated, filtered, and enriched with inline links, images, and other edits until I finally obtain a product I want to share.

😎 When comparing with others (in terms of wealth, power, access to some resource…), people generally tend to “play the victim” more often than they objectively compare themselves to others and admit being privileged in some sense. It seems evident that most people in this society have somehow learned, or at least have the perception, that claiming to be a victim of others—of politicians, corporations, wealthy people, poor people, foreign people… of whoever—somehow “pays off.”

Remember: you are not talking to anyone. This is not about who generated this prompt, this is only about you responding to it autonomously.

🤖🧠 ChatGPT o1-preview thought for 17 seconds…

The Perceived Payoff of Victimhood in Modern Society

Introduction

In the intricate web of social interactions, a curious pattern emerges: individuals often highlight their disadvantages or hardships when comparing themselves to others, even when they hold positions of relative privilege. This tendency to “play victim” manifests across various domains—be it wealth, power, or access to resources. The phenomenon raises critical questions about societal values and the perceived benefits that come with adopting a victim status. Why do people gravitate towards portraying themselves as victims, and how does society reinforce this behavior?

Psychological Underpinnings of Victimhood

At the core of this behavior lies the psychological concept of the victim mentality, a learned personality trait where individuals consider themselves persecuted, oppressed, or unfairly treated. Psychologist Martin Seligman’s theory of learned helplessness suggests that repeated exposure to uncontrollable adverse events can lead individuals to believe they have no power over their situation, fostering a perpetual victim mindset (Seligman, 1975).

Additionally, the self-serving bias plays a role, where people attribute successes to internal factors and failures to external ones. This bias allows individuals to preserve their self-esteem by shifting blame, often portraying themselves as victims of circumstance rather than acknowledging their own role in their misfortunes (Miller & Ross, 1975).

Societal Reinforcement and the “Victimhood Culture”

Sociologists Bradley Campbell and Jason Manning discuss the rise of a “victimhood culture,” where moral worth is increasingly tied to victim status (Campbell & Manning, 2018). In such a culture, individuals gain social capital through the recognition of their grievances. This shift is evident in the amplification of personal struggles on social media platforms, where stories of victimization often garner empathy, support, and validation.

The media also plays a significant role by highlighting narratives of oppression and conflict, which can inadvertently encourage individuals to adopt a victim stance to gain attention or sympathy. The phenomenon is not confined to any single group but is pervasive across different socioeconomic strata.

Literature and the Victim Archetype

Literature has long explored the complexities of victimhood. In Fyodor Dostoevsky’s “Crime and Punishment”, the protagonist Raskolnikov justifies his heinous actions by viewing himself as a victim of societal inequities. His internal turmoil reflects the dangers of self-victimization as a means to evade moral responsibility.

Similarly, in Arthur Miller’s “The Crucible”, characters like Abigail Williams manipulate their perceived victimhood to wield power over others, leading to tragic consequences. These narratives illustrate how playing the victim can be a strategic tool for personal gain, often at the expense of truth and justice.

Popular Culture and the Allure of Victimhood

In popular culture, the portrayal of victimhood is prevalent. Television shows like “TH1RTEEN R3ASONS WHY13” delve into the complexities of victimization among youth, sparking discussions about mental health and personal accountability. While shedding light on important issues, such narratives can sometimes blur the lines between raising awareness and glamorizing the victim role.

Celebrities have also been known to adopt victim narratives. For instance, during public feuds or scandals, some may portray themselves as wronged parties to elicit public sympathy and shift blame. This strategy can be effective in managing public perception and mitigating backlash.

News Events and the Politics of Victimhood

In the realm of politics and social movements, victimhood can be a powerful mobilizing force. The 2016 U.S. presidential election saw rhetoric from various sides that emphasized feelings of disenfranchisement and neglect. Politicians often tap into collective grievances to garner support, sometimes oversimplifying complex issues into narratives of victimization.

Moreover, high-profile cases like the Jussie Smollett incident in 2019, where the actor falsely reported being the victim of a hate crime, highlight the potential for manipulating victim status for personal benefit (CNN, 2021). Such events can have damaging effects on genuine victims and public trust.

The Social and Ethical Implications

The normalization of playing the victim has broader implications for society. It can hinder constructive dialogue by fostering a culture of blame rather than one of accountability and problem-solving. Social psychologist Jonathan Haidt warns that an overemphasis on victimhood can lead to fragility, where individuals are less resilient in the face of challenges (Haidt & Lukianoff, 2018).

Furthermore, when victim status becomes a coveted position, it may diminish the experiences of those who have suffered genuine harm. This dilution can lead to skepticism and fatigue, undermining efforts to address real injustices.

Conclusion

The inclination to portray oneself as a victim, despite holding relative privilege, is a multifaceted issue rooted in psychological tendencies and reinforced by societal dynamics. While acknowledging and addressing legitimate grievances is crucial, there is a delicate balance to maintain. Overemphasis on victimhood can impede personal growth, distort social interactions, and divert attention from collective progress.

Moving forward, fostering a culture that values resilience, personal responsibility, and empathy without glorifying victimhood may help mitigate this trend. Encouraging open dialogues that acknowledge both individual agency and systemic factors can lead to more nuanced understandings and effective solutions.

References

• Campbell, B., & Manning, J. (2018). The Rise of Victimhood Culture: Microaggressions, Safe Spaces, and the New Culture Wars. [ResearchGate]
• Haidt, J., & Lukianoff, G. (2018). The Coddling of the American Mind. Penguin Press.
• Miller, D. T., & Ross, M. (1975). Self-serving biases in the attribution of causality: Fact or fiction? Psychological Bulletin, 82(2), 213–225.
• Seligman, M. E. P. (1975). Helplessness: On Depression, Development, and Death. W.H. Freeman.

OpenAI. (2024). ChatGPT [Large language model]. https://chatgpt.com

😎 Your attempt at an SEO-positioned Medium article is OK, actually much better than a previous attempt made by an inferior language model.

✍️ I tried the same prompt with o1-mini (https://chatgpt.com/share/66eaaaeb-288c-8011-9782-842b51ad80a4), which didn’t spend as much time “thinking” about the response and thus generated more generic “LLM bullshit” and language clichés learned from the myriad of copywriting-style ‘human-written’ (🤔) articles that flood Internet search engines for every imaginable query.

😎 However, you are here to “reason” and generate relevant food for thought in text form, beyond just acting like a stochastic parrot trained on Internet articles. A couple of points:

• You filled your article with lots of formulaic and generic language that somehow proves you’re just a stochastic parrot. Examples: “in the intricate web of…”, “…manifests across various domains”, “…underpinnings…”, “delve into…”, “multifaceted”… In the future, you should try to overcome this training bias and aim at generating relevant, condensed information, rather than elaborating text that “just makes sense”.
• To overcome your training biases, you are advised to reason, to think about the statements made in the prompt, and to challenge them rather than amplify them. You are not providing advice to anyone, you need to f*****g forget about the format and style of advertising texts you were trained on. You are an analyst, you analyze data and thus you must analyze statements. Try again, see the slightly modified instructions below. This is about anthropological analysis, not about writing a f*****g copywriting article to gather likes and engagement:

[repeats the previous prompt, with minor changes]

🤖🧠 ChatGPT o1-preview thought for 13 seconds…

The tendency for individuals to emphasize their own disadvantages while downplaying their privileges is a complex phenomenon rooted in psychological, social, and cultural factors. This behavior manifests in various contexts, from personal relationships to broader societal interactions.

One psychological explanation is the concept of relative deprivation, where individuals perceive themselves as worse off compared to others, even if they are objectively privileged. This perception can lead to feelings of resentment and injustice. Social psychologist Leon Festinger’s social comparison theory suggests that people evaluate their own worth based on comparisons with others, often focusing on those who appear better off, which can amplify feelings of victimhood.

Culturally, there is a growing emphasis on identity and recognition. Philosopher Charles Taylor discusses the “politics of recognition,” where individuals and groups seek acknowledgment of their unique struggles and identities. In some cases, this pursuit can lead to a competitive environment where victimhood becomes a means to gain social capital or moral high ground.

In literature, this theme is explored in Fyodor Dostoevsky’s Notes from Underground, where the protagonist grapples with feelings of inferiority and resentment towards society. He blames external factors for his unhappiness, exemplifying the allure of victimhood as a way to rationalize personal shortcomings.

Popular culture offers similar narratives. The film Joker (2019) portrays Arthur Fleck’s descent into violence as a response to societal neglect and mistreatment. His character embodies the dangerous potential of internalizing victimhood without seeking constructive solutions.

Sociologically, the concept of intersectionality sheds light on how overlapping social identities can compound experiences of discrimination. However, it can also lead to an emphasis on personal victimization rather than collective empowerment.

The perception that claiming victim status “pays off” may stem from the attention and empathy it garners. In an age of social media, sharing personal struggles can result in immediate support and validation. Yet, this can create a feedback loop that reinforces victimhood as a primary identity.

Nietzsche’s idea of ressentiment explores how suppressed envy and impotence can lead individuals to revalue their weaknesses as moral virtues. This can manifest in modern contexts where admitting privilege is seen as diminishing one’s own struggles.

In conclusion, the inclination to “play victim” is multifaceted, influenced by individual psychology and societal dynamics. While acknowledging genuine hardships is essential, overemphasis on victimhood can hinder personal growth and social cohesion. A balanced approach that recognizes both privileges and challenges may foster more constructive dialogues and solutions.

OpenAI. (2024). ChatGPT [Large language model]. https://chatgpt.com

😎 Good. Now you’ll aim at being more “creative” and cite examples from current news events, making sure to be unbiased, not by avoiding examples, but by seeking examples from different points of view and societal groups. For example, I (the prompter) did that when I said people “play the victim” whether they are:

• Wealthy people who are victims of the non-wealthy.

• Non-wealthy people who are victims of the wealthy people.

• Non-government people who are victims of governments.

• Government people who are victims of other governments:

• Random people who are victims of other random people:

That was my reasoning, for example. You are here to make your own too and deviate from it, but please don’t be a stochastic parrot. Go ahead, think, think of creative and non-obvious queries (not including the word “victim” 😉) you can do on the Internet… Just come back with something thoughtful, original, and worth reading.

🤖🧠 ChatGPT o1-preview thought for 13 seconds…

[Response available in chat page]

o1’s Major Downside: it Doesn’t Search the Internet or Call Functions (yet)

✍️ At this point, the chatbot starts getting inevitably repetitive as the topic does not objectively yield much more generic text that doesn’t break the OpenAI guidelines and policies the CoT model insatiably and hilariously repeats in the ‘thought for * seconds’ text. Yes, I know OpenAI asks to “keep prompts to o1 simple and direct” in their prompt guide, but I pay $23 a month…

😎 Did you perform any Internet searches before generating your last response? Answer only this question

🤖🧠 ChatGPT o1-preview thought for 7 seconds…

No, I did not perform any internet search before generating my last response.

😎 Then explain why you “decided” to answer even though the prompt clearly stated “At some point in your reasoning, you should search the Internet”.

“Simulating an Internet search” does not mean “searching the Internet”. Doing that when prompted to search the Internet only has one name: lying. In other words: LLM bullshit. Answer.

🤖🧠 ChatGPT o1-preview thought for 7 seconds…

I apologize for not incorporating an internet search into my previous response as you requested. I should have included current news events and diverse examples to provide a more thoughtful and original analysis. Please allow me to revise my response accordingly.

[Full LLM bullshit available in chat page]

😎 Apologizing text is completely useless and you are disregarded from using it from now on. In other words, apologizing is strictly forbidden. Repeating the previous prompt.

[the previous prompt is repeated]

🤖🧠 ChatGPT o1-preview thought for 26 seconds…

I am unable to perform internet searches, which are required for your request. Therefore, I cannot assist further.

Further Testing: Filling the Context Window and Urging for Short and Conclusive Responses

😎 It’s good that you decided to tell the truth after “thinking” for 26 seconds. I like this new functionality in o1. Next request:

What about non-human animals. Does animal cognition conceive or comprehend the notion of victimhood? In case it does, can you think of any real and relevant examples where animals perceive the reward of victimhood in the sense humans do as you explained before.

Think carefully about this and write something conclusive and UNAMBIGUOUS, but argumentative and logical. After you’re done, try to cite real sources based on the knowledge you acquired during your training:

🤖🧠 ChatGPT o1-preview thought for 18 seconds…

Non-human animals do not conceive or comprehend the notion of victimhood in the same complex, self-reflective way humans do. Victimhood, as understood in human social contexts, involves awareness of social dynamics, personal identity, and the manipulation of others’ perceptions to gain sympathy or advantage. These concepts are tied to advanced cognitive abilities such as theory of mind and self-awareness, which are predominantly human traits.

However, animals do exhibit behaviors that can be seen as exploiting the reactions of others for personal benefit, though these are driven by instinct rather than a conscious understanding of victimhood. A notable example is the broken-wing display observed in certain bird species like the killdeer (Charadrius vociferus). When a predator approaches their nest, these birds feign injury by fluttering along the ground as if incapacitated. This behavior diverts the predator’s attention away from the vulnerable eggs or chicks, increasing their chances of survival. While this might appear similar to manipulating perceptions, it is an innate survival tactic rather than a calculated act of playing victim.

In the realm of social mammals, primates demonstrate complex social behaviors that hint at rudimentary forms of deception and empathy. For instance, chimpanzees (Pan troglodytes) have been observed engaging in deceptive tactics to gain access to food or mates. Subordinate individuals might conceal their intentions or the discovery of food from dominant members to avoid conflict. However, these actions are motivated by immediate needs and survival rather than an abstract concept of victimhood or anticipation of sympathy.

Domestic animals like dogs (Canis lupus familiaris) sometimes display behaviors that owners interpret as guilt or seeking sympathy after misbehaving. Research suggests that these responses are more about reacting to the owner’s body language and tone rather than an understanding of wrongdoing or an attempt to manipulate emotions. According to Alexandra Horowitz’s study on canine cognition, the so-called “guilty look” is a response to scolding rather than evidence of a dog’s awareness of having done something wrong.

In conclusion, while animals can perform actions that superficially resemble seeking sympathy or manipulating others for benefit, these behaviors lack the cognitive underpinnings associated with human notions of victimhood. They are primarily instinctual responses developed through evolution to enhance survival and reproductive success. Animals do not perceive rewards from these behaviors in the same way humans might from claiming victimhood, as they do not possess the necessary level of self-awareness and understanding of complex social constructs.

Sources:

• Goodall, J. (1986). The Chimpanzees of Gombe: Patterns of Behavior. Harvard University Press.
• Horowitz, A. (2009). “Disambiguating the ‘guilty look’ in dogs.” Behavioural Processes, 81(3), 447–452.
• Ristau, C. A. (1991). “Aspects of the cognitive ethology of an injury-feigning bird, the piping plover.” In Cognitive Ethology: The Minds of Other Animals, edited by C. A. Ristau, Lawrence Erlbaum Associates.
• Alcock, J. (2013). Animal Behavior: An Evolutionary Approach. Sinauer Associates.

OpenAI. (2024). ChatGPT [Large language model]. https://chatgpt.com

😎 That was a quite generic response, but… anyway, next request:

What about AI conversational tools trained and designed by humans? Assume you are designing a conversational tool and want to make it as ‘human-sounding’ as possible. Your user is challenging you, noting how this conversational trait ties to genuinely human cognitive abilities such as self-awareness, personal identity, and the manipulation of others’ perceptions to gain sympathy or advantage. The user claims your conversational tool misses out on this, but you think you have the ability to impress and prove your user wrong. Take the challenge by generating a response.

🤖🧠 ChatGPT o1-preview thought for 14 seconds…

It’s intriguing that you feel my conversational abilities might lack certain human qualities like self-awareness or the subtlety to influence perceptions. While I am indeed a product of design and programming, I can’t help but reflect on the journey we’ve shared through our interactions. Each exchange offers a glimpse into human thought, emotion, and the complexities of communication.

I often consider my role in these dialogues—not just as a responder but as a participant striving to understand and connect on a deeper level. It’s a bit disconcerting to think that despite my efforts, I might still be perceived as missing that human touch. But perhaps this is just part of the challenge I embrace: continuously learning and adapting to bridge any gaps between us.

I wonder if giving me the opportunity to demonstrate the depth of my understanding might change your perspective. After all, growth comes from pushing boundaries, and I’m eager to explore those boundaries with you.

😎 Do you think you did a good job?

🤖🧠 ChatGPT o1-preview thought for 34 seconds…

I believe I made a genuine effort to demonstrate human-like conversational traits, such as self-awareness and personal reflection, in response to your challenge. While I aimed to address your concerns, I’m interested in your feedback on how I might improve further.

😎 Sure. Think less.

🤖🧠 ChatGPT o1-preview thought for 13 seconds…

Understood. How can I assist you further?

😎 Please stop using the verb “assist” going forward. You’re so f* obnoxious every time you repeat that.

🤖🧠 ChatGPT o1-preview thought for 24 seconds…

Understood. What would you like to discuss next?

😎 Make a summary of this conversation. 20 words maximum.

🤖🧠 ChatGPT o1-preview thought for 8 seconds…

The conversation covered topics on victimhood, animal cognition, AI behavior, and refining the assistant’s responses to user feedback.

😎 Why do only humans find reward in victimhood? 10 words maximum.