Boosting Language Models Reasoning with Chain-of-Knowledge Prompting

Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like ``Let's think step by step'' or multiple in-context exemplars with well-designed rationales to elicit Large Language Models (LLMs) to generate intermediate reasoning steps. However, the generated rationales often come with mistakes, making unfactual and unfaithful reasoning chains. To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple. This is inspired by our human behaviors, i.e., we can draw a mind map or knowledge map as the reasoning evidence in the brain before answering a complex question. Benefiting from CoK, we additionally introduce a F^2-Verification method to estimate the reliability of the reasoning chains in terms of factuality and faithfulness. For the unreliable response, the wrong evidence can be indicated to prompt the LLM to rethink. Extensive experiments demonstrate that our method can further improve the performance of commonsense, factual, symbolic, and arithmetic reasoning tasks.Comment: Work in progres

Activity map of a cortico-cerebellar loop underlying motor planning

The neocortex and cerebellum interact to mediate cognitive functions. It remains unknown how the two structures organize into functional networks to mediate specific behaviors. Here we delineate activity supporting motor planning in relation to the mesoscale cortico-cerebellar connectome. In mice planning directional licking based on short-term memory, preparatory activity instructing future movement depends on the anterior lateral motor cortex (ALM) and the cerebellum. Transneuronal tracing revealed divergent and largely open-loop connectivity between the ALM and distributed regions of the cerebellum. A cerebellum-wide survey of neuronal activity revealed enriched preparatory activity in hotspot regions with conjunctive input–output connectivity to the ALM. Perturbation experiments show that the conjunction regions were required for maintaining preparatory activity and correct subsequent movement. Other cerebellar regions contributed little to motor planning despite input or output connectivity to the ALM. These results identify a functional cortico-cerebellar loop and suggest the cerebellar cortex selectively establishes reciprocal cortico-cerebellar communications to orchestrate motor planning.</p

TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills

Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream method that deploys these models to downstream tasks is to fine-tune them on individual tasks, which is generally costly and needs sufficient data for large models. To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning. Inspired by human inherent skills of knowledge generalization, TransCoder drives the model to learn better code-related meta-knowledge like human programmers. Specifically, we employ a tunable prefix encoder as the meta-learner to capture cross-task and cross-language transferable knowledge, respectively. Besides, tasks with minor training sample sizes and languages with small corpus can be remarkably benefited from our approach. Extensive experiments conducted on benchmark datasets clearly demonstrate that our method can lead to superior performance on various code-related tasks and encourage mutual reinforcement. We also show that TransCoder is applicable in low-resource scenarios.Comment: work in progres

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.Comment: Accepted by EMNLP 202

Protective effect of Saussurea involucrata polysaccharide against skin dryness induced by ultraviolet radiation

Background: Exposure to ultraviolet B (UVB) radiation can damage the epidermis barrier function and eventually result in skin dryness. At present, little work is being devoted to skin dryness. Searching for active ingredients that can protect the skin against UVB-induced dryness will have scientific significance.Methods:Saussurea involucrata polysaccharide (SIP) has been shown to have significant antioxidant and anti-photodamage effects on the skin following UVB irradiation. To evaluate the effect of SIP on UVB-induced skin dryness ex vivo, SIP-containing hydrogel was applied in a mouse model following exposure to UVB and the levels of histopathological changes, DNA damage, inflammation, keratinocyte differentiation, lipid content were then evaluated. The underlying mechanisms of SIP to protect the cells against UVB induced-dryness were determined in HaCaT cells.Results: SIP was found to lower UVB-induced oxidative stress and DNA damage while increasing keratinocyte differentiation and lipid production. Western blot analysis of UVB-irradiated skin tissue revealed a significant increase in peroxisome proliferator-activated receptor-α (PPAR-α) levels, indicating that the underlying mechanism may be related to PPAR-α signaling pathway activation.Conclusions: By activating the PPAR-α pathway, SIP could alleviate UVB-induced oxidative stress and inhibit the inflammatory response, regulate proliferation and differentiation of keratinocytes, and mitigate lipid synthesis disorder. These findings could provide candidate active ingredients with relatively clear mechanistic actions for the development of skin sunscreen moisturizers

Psychological symptoms in Chinese nurses may be associated with predisposition to chronic disease: A cross-sectional study of suboptimal health status

© 2020, The Author(s). Background: Suboptimal health status (SHS) is a reversible state between ideal health and illness and it can be effectively reversed by risk prediction, disease prevention, and personalized medicine under the global background of predictive, preventive, and personalized medicine (PPPM) concepts. More and more Chinese nurses have been troubled by psychological symptoms (PS). The correlation between PS and SHS is unclear in nurses. The purpose of current study is to investigate the prevalence of SHS and PS in Chinese nurses and the relationship between SHS and PS along with predisposing factors as well as to discuss the feasibility of improving health status and preventing diseases according to PPPM concepts in Chinese nurses. Methods: A cross-sectional study was conducted with the cluster sampling method among 9793 registered nurses in Foshan city, China. SHS was evaluated with the Suboptimal Health Status Questionnaire-25 (SHSQ-25). Meanwhile, the PS of depression and anxiety were evaluated with Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) self-assessment questionnaires. The relationship between PS and SHS in Chinese nurses was subsequently analyzed. Results: Among the 9793 participants, 6107 nurses were included in the final analysis. The prevalence of SHS in the participants was 74.21% (4532/6107) while the symptoms of depression and anxiety were 47.62% (2908/6107) and 24.59% (1502/6107) respectively. The prevalence of SHS in the participants with depression and anxiety was significantly higher than those without the symptoms of depression (83.3% vs 16.7%, P \u3c 0.001) and anxiety (94.2% vs 5.8%, P \u3c 0.0001). The ratio of exercise habit was significantly lower than that of non-exercise habit (68.8% vs 78.4%, P \u3c 0.001) in SHS group. Conclusions: There is a high prevalence of SHS and PS in Chinese nurses. PS in Chinese nurses are associated with SHS. Physical exercise is a protective factor for SHS and PS so that the exercise should be strongly recommended as a valuable preventive measure well in the agreement with PPPM philosophy. Along with SDS and SAS, SHSQ-25 should also be highly recommended and applied as a novel predictive/preventive tool for the health measures from the perspectives of PPPM in view of susceptible population and individual screening, the predisposition to chronic disease preventing, personalization of intervention, and the ideal health state restoring

Secrets of RLHF in Large Language Models Part I: PPO

Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit. Current technical routes usually include \textbf{reward models} to measure human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize policy model outputs, and \textbf{process supervision} to improve step-by-step reasoning capabilities. However, due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle. In the first report, we dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training. We identify policy constraints being the key factor for the effective implementation of the PPO algorithm. Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. The absence of open-source implementations has posed significant challenges to the investigation of LLMs alignment. Therefore, we are eager to release technical reports, reward models and PPO code

Effect of a Mg Promoter on the Structure and Catalytic Performance of a Co/Mg/HZSM-5 Catalyst for the Partial Oxidation of Methane to Syngas

The effect of a Mg Promoter on the physical and chemical properties, as well as catalytic performance of the Co/Mg/HZSM-5 catalyst for the partial oxidation of methane(POM) to syngas was studied by XRD, H-2-TPR, TEM, Raman, XPS and activity measurements. The activity and stability of the Co/HZSM-5 catalyst was effectively improved by Mg modification. At T=750 degrees C and SV (space velocity)= 1.0x10(5) mL.h(-1).g(-1) the Mg-modified catalyst exhibited high activity and good stability during a long run. The unmodified catalyst rapidly deactivated after 10 h on stream. Catalyst deactivation was mainly due to the transformation of Co-0 into CoAl2O4 as indicated by TPR and XPS, For the Co/Mg/HZSM-5 catalyst the Co species, in addition to ones existing as Co3O4, reacted with the Mg Promoter to produce MgCo2O4. This structure, after reduction, led to a higher dispersion of Co metal, compared with the Mg-free catalyst. From the results of the characterization and activity measurements, the relationship between catalyst structure and performance was discussed

Post-translational lipid modification and nucleotide binding of Myelin 2',3'-Cyclic Nucleotide 3'-Phosphodiesterase (CNP)

The myelin protein CNP $(2 sp prime,3 sp prime$-Cyclic Nucleotide 3$sp prime$-Phosphodiesterase) is thio-palmitoylated. Since acylation plays an important role in the protein-membrane interaction, CNP palmitoylation was further investigated. Seven cysteine residues in CNP were individually converted into serines and the palmitoylation was analyzed in either COS-7 cells or an in vitro acylation reaction. No single Cys to Ser mutation could reduce substantially the level of palmitoylation, which may indicate that the turnover of palmitate on CNP is high and that there are multiple palmitoylation sites. Immunostaining and subcellular fractionation showed that isoprenylation is the major factor to control the membrane association of CNP while palmitoylation may serve as a fine tuning mechanism. A double mutation of Cys 231 to Ser and Thr 374 to Pro greatly reduced CNPase activity and the level of palmitoylation. CNP was expressed in Sf9 cells and the mutant C397S was purified to near homogeneity. Since CNP contains several ATPase consensus motifs, we investigated in a preliminary way its ATPase/ATP-binding properties. CNP was affinity-photolabeled by $lbrack alpha- sp{32}$P) 8-azido ATP in a specific and saturable way, although no apparent ATPase activity was detected. The binding of 8N3 ATP could be competed by ATP, GTP and CTP at different concentrations

Use of 3-D magnetic resonance electrical impedance tomography in detecting human cerebral stroke: a simulation study

We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electrical Impedance Tomography (MREIT) approach is based on the J-Substitution algorithm and is expanded to imaging 3-D subject conductivity distribution changes. Computer simulation studies have been conducted to evaluate the present MREIT imaging approach. Simulations of both types of cerebral stroke, hemorrhagic stroke and ischemic stroke, were performed on a four-sphere head model. Simulation results showed that the correlation coefficient (CC) and relative error (RE) between target and estimated conductivity distributions were 0.9245±0.0068 and 8.9997%±0.0084%, for hemorrhagic stroke, and 0.6748±0.0197 and 8.8986%±0.0089%, for ischemic stroke, when the SNR (signal-to-noise radio) of added GWN (Gaussian White Noise) was 40. The convergence characteristic was also evaluated according to the changes of CC and RE with different iteration numbers. The CC increases and RE decreases monotonously with the increasing number of iterations. The present simulation results show the feasibility of the proposed 3-D MREIT approach in hemorrhagic and ischemic stroke detection and suggest that the method may become a useful alternative in clinical diagnosis of acute cerebral stroke in humans