Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving,
decision-making, and understanding the world. While large language models (LLMs) can …

Despite the notable advancements of existing prompting methods, such as In-Context
Learning and Chain-of-Thought for Large Language Models (LLMs), they still face …

Large language model (LLM) performance on reasoning problems typically does not
generalize out of distribution. Previous work has claimed that this can be mitigated by …

Large Language Models (LLMs) have demonstrated remarkable capabilities across various
tasks but their performance in complex logical reasoning tasks remains unsatisfactory …

Large language models (LLMs) have demonstrated limitations in handling combinatorial
optimization problems involving long-range reasoning, partially due to causal hallucinations …

Locating and fixing software faults is a time-consuming and resource-intensive task in
software development. Traditional fault localization methods, such as Spectrum-Based Fault …

We propose a variant of chain of thought (CoT) prompting called Program Trace Prompting
that makes explanations more observable while preserving the power, generality and …

Large language models (LLMs) have shown significant achievements in solving a wide
range of tasks. Recently, LLMs' capability to store, retrieve and infer with symbolic …

Large Language Models (LLMs) have recently advanced many applications on software
engineering tasks, particularly the potential for code generation. Among contemporary …

Chain-based reasoning methods like chain of thought (CoT) play a rising role in solving
reasoning tasks for large language models (LLMs). However, the causal illusions …