Chain of Recursive Thoughts (CoRT) proposes a method for improving large language models (LLMs) by prompting them to engage in self-debate. The LLM generates multiple distinct "thought" chains addressing a given problem, then synthesizes these into a final answer. Each thought chain incorporates criticisms of preceding chains, forcing the model to refine its reasoning and address potential flaws. This iterative process of generating, critiquing, and synthesizing promotes deeper reasoning and potentially leads to more accurate and nuanced outputs compared to standard single-pass generation.
Python's help() function provides interactive and flexible ways to explore documentation within the interpreter. It displays docstrings for objects, allowing you to examine modules, classes, functions, and methods. Beyond basic usage, help() offers several features like searching for specific terms within documentation, navigating related entries through hyperlinks (if your pager supports it), and viewing the source code of Python objects when available. It utilizes the pydoc module and works on live objects, not just names, reflecting runtime modifications like monkey-patching. While powerful, help() is best for interactive exploration and less suited for programmatic documentation access where inspect or pydoc modules provide better alternatives.
Hacker News users discussed the nuances and limitations of Python's help() function. Some found it useful for quick checks, especially for built-in functions, while others pointed out its shortcomings when dealing with more complex objects or third-party libraries, where docstrings are often incomplete or missing. The discussion touched upon the superiority of using dir() in conjunction with help(), the value of IPython's ? operator for introspection, and the frequent necessity of resorting to external documentation or source code. One commenter highlighted the awkwardness of help() requiring an object rather than a name, and another suggested the pydoc module or online documentation as more robust alternatives for exploration and learning. Several comments also emphasized the importance of well-written docstrings and recommended tools like Sphinx for generating documentation.
Summary of Comments ( 220 )
https://news.ycombinator.com/item?id=43835445
HN users discuss potential issues with the "Chain of Recursive Thoughts" approach. Some express skepticism about its effectiveness beyond simple tasks, citing the potential for hallucinations or getting stuck in unproductive loops. Others question the novelty, arguing that it resembles existing techniques like tree search or internal dialogue generation. A compelling comment highlights that the core idea – using a language model to critique and refine its own output – isn't new, but this implementation provides a structured framework for it. Several users suggest the method might be most effective for tasks requiring iterative refinement like code generation or mathematical proofs, while less suited for creative tasks. The lack of comparative benchmarks is also noted, making it difficult to assess the actual improvements offered by this method.
The Hacker News post "Chain of Recursive Thoughts: Make AI think harder by making it argue with itself" generated a moderate amount of discussion, with several commenters engaging with the core idea of the proposed "Chain of Recursive Thoughts" technique.
Several commenters expressed intrigue and interest in the concept. One commenter likened the process to "rubber ducking," a common debugging technique where explaining a problem aloud often reveals the solution. They suggested that the act of generating and refining thoughts recursively could similarly help the AI uncover flaws or inconsistencies in its reasoning. Another commenter pointed out the parallel to human thought processes, noting that we often refine our ideas by internally debating different perspectives. They saw the potential for this technique to lead to more nuanced and robust AI outputs.
Some commenters raised concerns and questions. One questioned the practicality of the approach, particularly regarding the computational resources required for repeated iterations of thought generation. They wondered if the benefits of improved reasoning would outweigh the increased computational cost. Another commenter expressed skepticism about the novelty of the idea, arguing that similar techniques involving self-reflection and refinement have already been explored in AI research. They requested clarification on how "Chain of Recursive Thoughts" differed from existing methods.
Another line of discussion revolved around the potential for unintended consequences. One commenter raised the concern that this recursive process could amplify biases present in the initial prompt or the AI model itself. They argued that without careful consideration, the AI might become entrenched in flawed reasoning, rather than correcting it. Another commenter speculated about the possibility of the AI getting "stuck" in a loop, endlessly refining its thoughts without reaching a meaningful conclusion.
One commenter offered a practical suggestion for evaluating the effectiveness of the technique. They proposed testing it on logical reasoning problems where the correct answer is known. This, they argued, would provide a clear metric for assessing whether the recursive thought process leads to improved problem-solving abilities.
While generally receptive to the core idea, the comments highlighted both the potential benefits and the potential pitfalls of the "Chain of Recursive Thoughts" technique. The discussion emphasized the need for further research and experimentation to fully understand its implications and effectiveness.