The contemporary LLMs are prone to producing hallucinations, stemming mainly from the
knowledge gaps within the models. To address this critical limitation, researchers employ …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Large language models (LMs) are capable of generating free-text rationales to aid question
answering. However, prior work 1) suggests that useful self-rationalization is emergent only …

Prompt Engineering has garnered significant attention for enhancing the performance of
large language models across a multitude of tasks. Techniques such as the Chain-of …

Chain-of-thought (CoT) prompting with large language models has proven effective in
numerous natural language processing tasks, but designing prompts that generalize well to …

To comprehensively gauge the capacity of current models for complex reasoning, it is crucial
to assess their step-by-step reasoning in a scalable manner. Established reference-based …

With recent advancements in large language models, methods like chain-of-thought
prompting to elicit reasoning chains have been shown to improve results on reasoning …

The leaderboard of Large Language Models (LLMs) in mathematical tasks has been
continuously updated. However, the majority of evaluations focus solely on the final results …

In this study, we delve into the realm of counterfactual reasoning capabilities of large
language models (LLMs). Our primary objective is to cultivate the counterfactual thought …

Generating accurate step-by-step reasoning is essential for Large Language Models (LLMs)
to address complex problems and enhance robustness and interpretability. Despite the flux …