Larger Language Models Likely Unable to Adjust Their own Line of Thought? Highly Unlikely.
In a recent study, researchers from Google DeepMind and the University of Illinois examined the potential for large language models (LLMs) like GPT-3, PaLM, and ChatGPT to self-correct their own mistakes and flawed reasoning. The findings suggest that while some progress has been made, LLMs still struggle with effectively combining error detection and meaningful self-correction in reasoning tasks.
The study, which focused on "intrinsic self-correction" where models attempt to fix mistakes without any external feedback or assistance, revealed that current LLMs struggle to self-correct. In fact, their performance often deteriorates after attempting correction. This is particularly evident in complex problem-solving, especially in math and reasoning tasks, where LLMs often fail to recognize when problems are ill-posed or unreasonable.
One of the key issues is that LLMs often fall into "reasoning spirals," where repeated verification efforts and self-checking cycles actually move them further from the solution instead of improving it. This can consume large amounts of tokens and lead to verbose but unproductive responses. Another challenge is that models that show increased sensitivity to unreasonable inputs often produce overly long, incoherent, or meaningless self-corrections.
Despite these limitations, self-correction shows the most promise on tasks where LLMs can judge response quality on concrete criteria. However, the researchers caution that self-correction should not be oversold as a cure-all for deficiencies in LLM reasoning.
The study also investigated more sophisticated self-correction techniques involving critique and debate between multiple LLM instances. While the multi-agent debate approach using 3 agents and 2 rounds of debate achieved 83.2% accuracy on GSM8K, it was only slightly better than a simpler self-consistency method where multiple independent responses are generated and majority voting used to select the final answer. With more responses, self-consistency significantly outperformed multi-agent debate.
The study concludes that the observed improvements are not attributable to "self-correction," but rather the self-consistency obtained across multiple generations. This suggests that feedback from humans, training data, and tools is still crucial for genuine reasoning improvements.
In light of these findings, the researchers suggest that focusing more on enhancing initial prompts than relying on post-hoc self-correction may be beneficial. They also note that techniques incorporating external guidance are probably needed to improve reasoning abilities.
The study was conducted across diverse reasoning tasks including mathematical word problems, common sense reasoning, and open-domain question answering datasets. The results reveal a clear need for improved frameworks that tightly integrate reasoning validation with concise and accurate revision mechanisms. As the use of LLMs continues to grow, addressing these limitations will be crucial for ensuring their reliability and effectiveness.
Artificial-intelligence powered large language models (LLMs) like GPT-3, PaLM, and ChatGPT have shown some progress in attempting to self-correct their own mistakes, but they still struggle with effectively combining error detection and meaningful self-correction, especially in complex problem-solving tasks such as math and reasoning.
Intrinsic self-correction techniques, where models attempt to fix mistakes without any external feedback or assistance, have been found to deteriorate the performance of LLMs rather than improve it, often leading to long, incoherent, or meaningless self-corrections.
