4 accepted papers
Large language models (LLMs) can achieve superior results through iterative refinement based on internal or external signals, compared to the unstable outputs from a single pass. However, the reliability of existing internal signals is questionable due to their susceptibility to intrinsic hallucinat…
Chain-of-thought (CoT) and subsequent methods adopted a deductive paradigm that decomposes the reasoning process, demonstrating remarkable performances across NLP tasks. However, such a paradigm faces the challenge of getting bogged down in low-level semantic details, hindering large language models…
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, the crucial role of problem context might have been neglected. Accurate recognition of inputs is fundamental for solving m…
By leveraging effective signals to refine their outputs, large language models (LLMs) can achieve superior performance compared to single-pass outputs. However, internal signals often suffer from accumulated hallucinations and a lack of confidence, while external signals are typically difficult to o…