ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models

Theory of Mind (ToM) refers to the ability of individuals to attribute mental states to others. While Large Language Models (LLMs) have shown some promise with ToM ability, they still struggle with complex ToM reasoning. Our approach leverages an external symbolic executor, specifically the SMCDEL model checker, and fine-tuning to improve the ToM reasoning ability of LLMs. In our approach, an LLM is first fine-tuned through pairs of natural language and symbolic formulation representation of ToM problems and is then instructed to generate the symbolic formulation with a one-shot in-context example. The generated symbolic formulation is then executed by the SMCDEL model checker to perform transparent and verifiable ToM reasoning and give the final result. We demonstrate that our approach, ToM-LM, shows a significant improvement over all the constructed baselines. Our study proposes a novel view about externalizing a particular component of ToM reasoning, mainly reasoning about beliefs, and suggests generalizing it to other aspects of ToM reasoning.Comment: Accepted at NeSy 202

Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models

Large Language Models (LLMs) have recently shown a promise and emergence of Theory of Mind (ToM) ability and even outperform humans in certain ToM tasks. To evaluate and extend the boundaries of the ToM reasoning ability of LLMs, we propose a novel concept, taxonomy, and framework, the ToM reasoning with Zero, Finite, and Infinite Belief History and develop a multi-round text-based game, called $\textit{Pick the Right Stuff}$, as a benchmark. We have evaluated six LLMs with this game and found their performance on Zero Belief History is consistently better than on Finite Belief History. In addition, we have found two of the models with small parameter sizes outperform all the evaluated models with large parameter sizes. We expect this work to pave the way for future ToM benchmark development and also for the promotion and development of more complex AI agents or systems which are required to be equipped with more complex ToM reasoning ability

LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language Models

Temporal reasoning (TR) is a critical component of artificial intelligence, encompassing understanding and processing temporal information and relationships between events. To discover and study the TR ability in Large Language Models (LLMs), various datasets have been constructed in different ways for evaluating various aspects of TR ability. Our work proposes a novel approach to design and develop a pipeline for constructing datasets to evaluate the TR ability of LLMs by leveraging random directed graph generation, LTL formula, and the NuSMV model checker. Based on the pipeline, we have also constructed a dataset as a benchmark, namely LTLBench, consisting of 2,000 TR challenges and evaluated six LLMs with it. Furthermore, we have conducted additional experiments to discover the impact of increasing the number of events and formula operators on the complexity of TR problems and the performance of LLMs. We have demonstrated that although LLMs exhibit some promise in handling TR challenges, they still struggle with complex TR. We expect this work can offer insights into TR ability in LLMs while also providing a valuable tool for future TR evaluations

HyGenar:An LLM-driven hybrid genetic algorithm for few-shot grammar generation

Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs. Although large language models (LLMs) demonstrate impressive capabilities across domains, their ability to infer and generate grammars has not yet been thoroughly explored. In this paper, we aim to study and improve the ability of LLMs for few-shot grammar generation, where grammars are inferred from sets of a small number of positive and negative examples and generated in Backus-Naur Form. To explore this, we introduced a novel dataset comprising 540 structured grammar generation challenges, devised 6 metrics, and evaluated 8 various LLMs against it. Our findings reveal that existing LLMs perform sub-optimally in grammar generation. To address this, we propose an LLM-driven hybrid genetic algorithm, namely HyGenar, to optimize grammar generation. HyGenar achieves substantial improvements in both the syntactic and semantic correctness of generated grammars across LLMs. The code is open-source and available at https://github.com/RutaTang/HyGenar

D4^4M: Dataset Distillation via Disentangled Diffusion Model

Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce Dataset Distillation via Disentangled Diffusion Model (D$^4$M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D$^4$M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D$^4$M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects.Comment: Accepted to CVPR 202

Cardiovascular Risk Factors and its Transition: An Ongoing Cohort Study in Chinese Kazakhs

Studies on the prevalence of risk factors and the incidence for cardiovascular diseases (CVDs) are limited in Kazakh population. By incorporating nomads, farmers, and urban residents, aged 30 years or older, in a cohort study, we investigated the characteristics of cardiovascular risk factors and their temporal trends that arose from the urbanization and subsequent changes in the lifestyle in a Kazakh population with 1668 participants. We used current guidelines and the monitoring trends and determinants in cardiovascular disease (MONICA) standard to define cardiovascular events. Kazakhs had a high prevalence rate of hypertension (45.3%), and this prevalence was much higher than the national average in China. Prevalence of two or more risk factors was highest among urban people and lowest among nomads. Urban residents have the highest prevalence of hypercholesterolemia and obesity compared with farmers and nomads. However, unlike other studies, our data indicate that young men had the highest prevalence of dyslipidemia, and it decreased significantly thereafter. Crude rates of incidence and mortality for acute cardiovascular events were 742 and 194 per 100,000 people, respectively; the standardized rates were 926 and 272 per 100,000 people, respectively. The findings from this study demonstrate the pervasive burden of cardiovascular risk factors and the related acute cardiovascular events in Kazakhs, particularly BP in Kazakh nomads

EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems

Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. This library provides lightweight and diverse RL environments based on five public datasets and includes core modules with rich options, simplifying model development. It provides unified evaluation standards focusing on long-term outcomes and offers tailored designs for state modeling and action representation for recommendation scenarios. Furthermore, we share our findings from insightful experiments with current methods. EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs. The library is available for public use.Comment: Accepted by SIGIR202

β-Adrenergic Receptor-PI3K Signaling Crosstalk in Mouse Heart: Elucidation of Immediate Downstream Signaling Cascades

Sustained β-adrenergic receptors (βAR) activation leads to cardiac hypertrophy and prevents left ventricular (LV) atrophy during LV unloading. The immediate signaling pathways downstream from βAR stimulation, however, have not been well investigated. The current study was to examine the early cardiac signaling mechanism(s) following βAR stimulation. In adult C57BL/6 mice, acute βAR stimulation induced significant increases in PI3K activity and activation of Akt and ERK1/2 in the heart, but not in lungs or livers. In contrast, the same treatment did not elicit these changes in β1/β2AR double knockout mice. We further showed the specificity of β2AR in this crosstalk as treatment with formoterol, a β2AR-selective agonist, but not dobutamine, a predominantly β1AR agonist, activated cardiac Akt and ERK1/2. Acute βAR stimulation also significantly increased the phosphorylation of mTOR (the mammalian target of rapamycin), P70S6K, ribosomal protein S6, GSK-3α/β (glycogen synthase kinase-3α/β), and FOXO1/3a (the forkhead box family of transcription factors 1 and 3a). Moreover, acute βAR stimulation time-dependently decreased the mRNA levels of the muscle-specific E3 ligases atrogin-1 and muscle ring finger protein-1 (MuRF1) in mouse heart. Our results indicate that acute βAR stimulation in vivo affects multiple cardiac signaling cascades, including the PI3K signaling pathway, ERK1/2, atrogin-1 and MuRF1. These data 1) provide convincing evidence for the crosstalk between βAR and PI3K signaling pathways; 2) confirm the β2AR specificity in this crosstalk in vivo; and 3) identify novel signaling factors involved in cardiac hypertrophy and LV unloading. Understanding of the intricate interplay between β2AR activation and these signaling cascades should provide critical clues to the pathogenesis of cardiac hypertrophy and enable identification of targets for early clinical interaction of cardiac lesions

Generation and characterization of rabbit embryonic stem cells

We described the derivation of four stable pluripotent rabbit embryonic stem cell (ESC) lines, one (RF) from blastocysts fertilized in vivo and cultured in vitro and three (RP01, RP02, and RP03) from parthenogenetic blastocysts. These ESC lines have been cultivated for extended periods (RF \u3e1 year, RP01 \u3e8 months, RP02 \u3e8 months, and RP03 \u3e6 months) in vitro while maintaining expression of pluripotent ESC markers and a normal XY or XX karyotype. The ESCs from all lines expressed alkaline phosphatase, transcription factor Oct-4, stage-specific embryonic antigens (SSEA-1, SSEA-3, and SSEA-4), and the tumor-related antigens (TRA-1-60 and TRA-1-81). Similar to human and mouse ESCs, rabbit ESCs expressed pluripotency (Oct-4, Nanog, SOX2, and UTF-1) and signaling pathway genes (fibroblast growth factor, WNT, and transforming growth factor pathway). Morphologically, rabbit ESCs resembled primate ESCs, whereas their proliferation characteristics were more like those seen in mouse ESCs. Rabbit ESCs were induced to differentiate into many cell types in vitro and formed teratomas with derivatives of the three major germ layers in vivo when injected into severe combined immunodeficient mice. Our results showed that pluripotent, stable ESC lines could be derived from fertilized and parthenote-derived rabbit embryos. ©AlphaMed Press