Peng Li

Hejia Geng, Boxun Xu, Peng Li

Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite these efforts, a unified epistemological foundation is still conspicuously absent. Drawing inspiration from Kant's a priori philosophy, we propose the UPAR prompting framework, designed to emulate the structure of human cognition within LLMs. The UPAR framework is delineated into four phases: "Understand", "Plan", "Act", and "Reflect", enabling the extraction of structured information from complex contexts, prior planning of solutions, execution according to plan, and self-reflection. This structure significantly augments the explainability and accuracy of LLM inference, producing a human-understandable and inspectable inferential trajectory. Furthermore, our work offers an epistemological foundation for existing prompting techniques, allowing for a possible systematic integration of these methods. With GPT-4, our approach elevates the accuracy from COT baseline of 22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in the causal judgment task. Without using few-shot examples or external tools, UPAR significantly outperforms existing prompting methods on SCIBENCH, a challenging dataset containing collegiate-level mathematics, chemistry, and physics scientific problems.

Qiu Ran, Yankai Lin, Peng Li et al.

Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage. Our system achieves an EM-score of 64.56% on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers.

Dafydd Gibbon, Peng Li

The objective of the present study is exploratory: to introduce and apply a new theory of speech rhythm zones or rhythm formants (R-formants). R-formants are zones of high magnitude frequencies in the low frequency (LF) long-term spectrum (LTS), rather like formants in the short-term spectra of vowels and consonants. After an illustration of the method, an R-formant analysis is made of non-elicited extracts from public speeches. The LF-LTS of three domains, the amplitude modulated (AM) absolute (rectified) signal, the amplitude envelope modulation (AEM) and frequency modulation (FM, F0, 'pitch') of the signal are compared. The first two correlate well, but the third does not correlate consistently with the other two, presumably due to variability of tone, pitch accent and intonation. Consequently, only the LF LTS of the absolute speech signal is used in the empirical analysis. An informal discussion of the relation between R-formant patterns and utterance structure and a selection of pragmatic variables over the same utterances showed some trends for R-formant functionality and thus useful directions for future research.