Hello Codeforces,
This is my first blog post on this platform and I think I have a question/discussion topic some programmers here may find interesting.
While currently LLMs still seem quite weak at reasoning-heavy tasks like difficult competitive programming problems, there are certainly use cases one can think of to take advantage of large language model (LLM) agents for problem solving on a platform like Codeforces.
Is it (or perhaps should it be) allowed under the Codeforces ruleset to make use of LLMs such as those published by OpenAI and Anthropic to assist in solving competitive programming problems?
There are already some existing blogs (eg. Anti-Plagiarism Proposal and Code Snippet Generation) about this topic, but there does not seem to be a unanimous consensus among the community (as far as I'm aware) and therefore I am writing this to seek clarification and maybe spark some discussion on the matter. I have read one opinion that because LLMs such as GPT-4 are widely accessible, it does not violate rule 2 in this blog which states that it is allowed to use code "generated using tools that were written and published/distributed before the start of the round". However, I am not sure this is very convincing (especially since this ruleset is so old).
I personally believe that while transformer-based model architectures have their limits, at some point LLMs will become powerful enough that they may require an explicit ban as it would take away from the competitive aspect of programming contests (similar to chess engines). However, I can also understand the argument that in their current state, LLMs are a healthy set of tools which may be used (and requires some skill to be used effectively) to augment the abilities of a human programmer, and thus should be allowed in contests.
Thanks to anyone who took the time to read this. I would be interested in hearing opinions/clarifications on the current contest ruleset.
Thank you for the insightful discussion topic regarding the utilization of large language models (LLMs) in competitive programming. It is indeed an area ripe for exploration and debate.
As you mentioned, the current capabilities of LLMs, while impressive, often fall short in tackling the most complex reasoning tasks associated with competitive programming. Nonetheless, their potential as a supplementary tool cannot be overlooked. The question of their permissibility under the Codeforces ruleset is a nuanced one.
Drawing upon the existing rules, particularly the interpretation of Rule 2, one could argue that LLMs fall within the bounds of permissible tools, given that they are pre-existing and widely accessible. However, this perspective might not be universally persuasive, considering the evolving landscape of artificial intelligence and the relatively static nature of the ruleset.
There is a parallel to be drawn with the domain of chess, where the advent of powerful engines necessitated a reevaluation of competition rules to preserve the integrity of human skill-based contests. Similarly, as LLMs advance, a reassessment may be warranted to ensure that the core competitive spirit of programming contests is maintained.
It is also worth noting that, in their current form, LLMs require a certain level of expertise to harness effectively. This could be seen as adding an additional layer of skill, akin to utilizing advanced algorithms or data structures knowledge, thereby augmenting rather than diminishing the competitive element.
I appreciate the thoughtful consideration of this matter and look forward to the diverse viewpoints from the community, which will undoubtedly enrich the ongoing discourse on this topic.
Thank you for your astute reflections on the utilization of large language models (LLMs) in the realm of competitive programming. It's a compelling topic that indeed warrants thorough examination and deliberation.
You highlight a critical point: while LLMs exhibit remarkable capabilities, their proficiency in managing the intricate reasoning required for high-level competitive programming is still evolving. Nevertheless, the prospect of using them as auxiliary tools introduces an intriguing dynamic.
Regarding the permissibility of LLMs under Codeforces' Rule 2, it's an interpretative matter. The rule presently accommodates the use of pre-existing and widely accessible tools, which ostensibly includes LLMs. Yet, the rapid progression of AI technology, juxtaposed with the relatively unchanged rule framework, raises questions about whether the current rules suffice to address the emerging challenges and opportunities presented by LLMs.
The analogy to chess is apt—chess engines revolutionized the game, prompting a reevaluation of competitive standards to safeguard the essence of human intellectual competition. Similarly, the advancement of LLMs may necessitate a reexamination of programming contest rules to uphold the integrity and foundational spirit of these competitions.
Furthermore, the effective use of LLMs demands a sophisticated understanding, parallel to the mastery of advanced algorithms and data structures. This implies that integrating LLMs could potentially enhance the competitive landscape by introducing an additional layer of complexity and skill.
Ultimately, fostering this discussion within the community is crucial. Diverse perspectives will undoubtedly enrich our understanding and help shape a balanced approach that respects both technological advancements and the core values of competitive programming.
Thank you once again for your thoughtful insights. I am eager to see how the conversation evolves and the community's input on navigating this intersection of AI and competitive programming.
Thank you for your insightful exploration of the role of large language models (LLMs) in competitive programming. You've raised crucial points about their current capabilities and the ethical considerations surrounding their use under existing competition rules.
Indeed, the evolving landscape of AI technology, akin to the impact of chess engines on chess, prompts us to carefully reconsider how we integrate such tools into competitive environments. As LLMs become more sophisticated, they offer both opportunities and challenges that necessitate a nuanced approach to maintain the integrity and spirit of programming contests.
Your analogy to Codeforces' Rule 2 is particularly pertinent. While the rule accommodates existing tools, including LLMs by extension, the rapid pace of AI advancement begs the question of whether current regulations adequately address the implications of these technologies. This calls for a community-wide dialogue to ensure a balanced approach that fosters innovation while preserving fair competition.
I appreciate your thoughtful perspective on this complex issue. It's clear that discussions like these are essential to shaping policies that uphold the principles of competitive programming in the face of technological progress. I look forward to seeing how this conversation unfolds and to hearing more voices from the community on this critical intersection of AI and programming competitions.
Your blog post raises an interesting and timely topic, especially as the capabilities of large language models (LLMs) continue to evolve. Here are some thoughts on the points you brought up:
Firstly, it's true that LLMs like GPT-4 still struggle with complex reasoning tasks inherent to competitive programming. While they excel at generating boilerplate code or solving simpler problems, they don't yet match a skilled programmer's problem-solving abilities. This means that, for now, LLMs can enhance productivity but not replace human expertise in this domain.
The current ambiguity in Codeforces' rules regarding the use of LLMs is another important issue. Rule 2, which allows the use of pre-existing tools, could be interpreted to include LLMs, but this might not align with the original intent of the rule. Given the rapid advancements in AI, a reevaluation of these rules seems necessary to ensure they remain relevant and fair.
If LLMs continue to improve, they might reach a point where their use in competitions undermines the core objective of assessing individual problem-solving skills. Drawing parallels to chess engines is apt; just as chess competitions had to adapt to the rise of powerful engines, programming contests might need to implement similar measures to preserve their competitive integrity.
For now, LLMs can be seen as tools that enhance a programmer's capabilities, much like an IDE's autocomplete feature or a debugger. However, there's a fine line between augmentation and substitution of skills. The competitive aspect of programming contests should focus on individual merit, which could be compromised if LLMs are over-relied upon.
Engaging the community in this discussion is crucial. A consensus or a clear set of guidelines will help maintain the integrity of competitions. Your initiative to seek opinions and clarifications is a step in the right direction, as it can spark a necessary debate and lead to well-informed decisions regarding the use of LLMs in competitive programming.
In summary, your post effectively highlights the need for a nuanced discussion about the role of LLMs in competitive programming. Balancing the benefits of technological aids with the principles of fair competition will be key as the capabilities of these models continue to grow.
I ain't reading alla't.
I ain't reading alla't.
the irony of fate
Your comment raises a meaningful and intriguing point regarding the usage of large language models (LLMs) in competitive programming, an especially relevant topic in today's climate as LLMs continue to adapt. Here are my thoughts on the matter.
I concur with your belief that LLMs struggle with complex reasoning tasks frequently found in competitive programming problems. They do possess the ability to write functioning code for more simple tasks and can provide template code for well-known algorithms, but they do lack proper problem-solving skills that are necessary to excel in competitive programming.
Rule 2, despite technically allowing for usage of LLMs due to their widespread availability, was clearly not built with such a possibility in mind. It should perhaps be revised to exclude LLMs for the sake of fairness.
The analogy to chess engines is quite fitting; although our current LLMs struggle to solve problems much like the earliest chess engines struggled to win matches, as they continue to grow and evolve their abilities may surpass those of humans, forcing programming contests to ban them to preserve the spirit of competition.
As it stands in the present, LLMs can be used as assistive tools at best, but its abilities grow stronger by the day. LLMs like GPT-4 have already pervaded through most of modern society, with competitive programming as one of the last remaining bastions where man is superior to machine. However, this final bastion may soon crumble if we as programmers continue to rely on LLMs.
It is vitally important to shed light on this matter; a final consensus must be reached to decide whether or not LLMs should be allowed. All are welcome to share their opinions to explain why LLMs should or should not be allowed in competitive programming.
help that took way too long to write
Thank you for your insightful analysis of the integration of large language models (LLMs) into competitive programming. Your observations highlight significant challenges LLMs encounter when tasked with complex problem-solving typical of competitive environments, despite their proficiency in generating code and templates for simpler algorithms.
Your suggestion to reassess rules, such as Rule 2, which allows for widely available tools but may not have anticipated LLMs, underscores the critical importance of ensuring fairness and upholding the competitive spirit. Drawing parallels to the evolution of chess engines effectively illustrates the potential trajectory of LLMs in competitive programming, prompting essential discussions about their role and potential implications.
Your emphasis on the current supportive role of LLMs and the ethical considerations surrounding their integration into competitive settings encourages a broader discourse on navigating these advancing technologies while preserving the integrity of human-driven problem-solving in programming contests.
As LLMs like GPT-4 continue to gain prominence across various domains traditionally reliant on human expertise, the debate surrounding their inclusion in competitive programming becomes increasingly pressing. While currently valuable as aids, their advancing capabilities pose challenges to established norms of equitable competition. The comparison to chess engines evolving from novice status to surpassing human grandmasters prompts reflection on whether analogous advancements in LLMs could necessitate their exclusion from specific competitive arenas to safeguard the essence of human achievement and innovation.
Furthermore, your insights into the ethical dimensions of this issue underscore the importance of establishing clear boundaries and guidelines. Adapting rules and regulations alongside technological progress will be crucial in maintaining a level playing field while leveraging the potential benefits LLMs offer in enhancing creativity and efficiency in problem-solving scenarios. Ultimately, reaching consensus on the appropriate role of LLMs in competitive programming will require a balanced approach that embraces innovation while preserving the fundamental aspects of human-driven intellectual competition.
I am deeply impressed by the intellectual discourse and profound insights exhibited in the comment chain. The thoughtful nature of the discussion demonstrates a remarkable level of cognitive engagement and analytical prowess. The participants have seamlessly integrated complex concepts related to large language models and competitive programming, showcasing an admirable synthesis of cutting-edge technology and algorithmic expertise. This exchange serves as a testament to the power of collaborative knowledge sharing in the digital age, fostering an environment conducive to innovation and intellectual growth. The nuanced perspectives presented herein undoubtedly contribute to the advancement of our collective understanding of artificial intelligence's role in enhancing human problem-solving capabilities.
*it took too long for chatgpt to write
no i actually did write it
wtf pro writer and ap lang shredder
in 1 week ap scores come out and i will get a 2
:lying_face: multiply it by 2.5 and that's the score you'll actually get
These comments are high quality banter lol
These comments seem GPT'd ;)
hey, that comment was 100% handwritten by me!
(it actually was)
Just joking around homie chilll
maxrgby orz
:lying_face: alternet orz
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average llm user
pathetique orz
The statement "pathetique orz" by user eysbutno necessitates a comprehensive refutation, particularly in establishing that "pathetique" cannot be equated with "orz." This intricate task requires a deep dive into the nuanced meanings and cultural contexts of these terms.
First and foremost, "pathetique," a term deeply rooted in the French language, translates to "pathetic" in English but carries with it a weight of dignified sorrow and profound emotional depth. It is most famously associated with Beethoven's Piano Sonata No. 8, Op. 13, known as the "Pathétique Sonata." This sonata encapsulates a complex emotional narrative, evoking a poignant, tragic beauty that resonates with listeners through its intricate composition and expressive power. The term "pathetique," therefore, is not merely a descriptor of superficial sadness; it is emblematic of a deeply moving and artistically significant emotional state.
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In conclusion, the assertion that "pathetique is not orz" holds true when we examine the depth, cultural significance, and contexts of these terms. "Pathetique" embodies a rich tapestry of emotional and historical resonance, far surpassing the immediate, albeit expressive, nature of "orz." While both terms effectively communicate states of human emotion, they do so on vastly different planes of significance. Therefore, it is clear that "pathetique" cannot be equated with "orz," as the former's profound emotional depth and historical context elevate it far beyond the reach of the latter's digital shorthand expression.
I wonder which LLM did these commenters use to generate these comments, lol.
Hm
Omg very lengthy comments.
Since nobody has mentioned this yet, let me put links to the information about the recent AtCoder's decisions on the use of generative AIs, for your reference: