Unlocking Language Models’ Reasoning Abilities

In this paper, the authors propose PATHFINDER, a method to improve text generation by imposing logical constraints on large language models. The goal is to enhance control, consistency, and accuracy in tasks that require step-by-step reasoning, such as arithmetic reasoning. The approach is based on the idea of decomposing complex tasks into simpler ones and using self-evaluation guided decoding to generate high-quality text.
The authors begin by highlighting the potential benefits and risks associated with text generation applications. They acknowledge that even under constraints, language models could potentially produce biased or offensive narratives. Therefore, it is crucial to carefully evaluate and analyze these risks.
Next, the authors present related work on decoding strategies for text generation. They explain that traditional methods such as greedy decoding and beam search offer high-quality results but lack diversity and are prone to getting stuck in local optima. To address this issue, researchers have proposed various alternative decoding methods, including non-uniform sampling and iterative refinement.
The authors then introduce PATHFINDER, which improves text generation by incorporating logical constraints into the decoding process. The approach is based on decomposing complex tasks into simpler ones and using self-evaluation guided decoding to generate high-quality text. The authors demonstrate that PATHFINDER outperforms existing methods in generating accurate and diverse text, particularly in step-by-step reasoning tasks.
Finally, the authors discuss ethical considerations and limitations of their approach. They acknowledge that any language model, even under constraints, could potentially produce biased or offensive narratives. Therefore, it is crucial to carefully evaluate and analyze these risks. The authors emphasize the need for ongoing research in this area to develop more robust and ethical text generation methods.
In conclusion, PATHFINDER offers a promising approach to improving text generation by incorporating logical constraints into the decoding process. By decomposing complex tasks into simpler ones and using self-evaluation guided decoding, PATHFINDER generates high-quality text that is both accurate and diverse. However, the authors also acknowledge the potential risks associated with text generation applications and emphasize the need for ongoing research to develop more robust and ethical methods.