R3^3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context

With the help of Chain-of-Thought (CoT) prompting, Large Language Models (LLMs) have achieved remarkable performance on various reasoning tasks. However, most of them have been evaluated under noise-free context and the dilemma for LLMs to produce inaccurate results under the noisy context has not been fully investigated. Existing studies utilize trigger sentences to encourage LLMs to concentrate on the relevant information but the trigger has limited effect on final answer prediction. Inspired by interactive CoT method, where intermediate reasoning steps are promoted by multiple rounds of interaction between users and LLMs, we propose a novel prompting method, namely R$^3$ prompting, for CoT reasoning under noisy context. Specifically, R$^3$ prompting interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction, which corresponds to a thought process of reviewing, rephrasing and resolving. The responses generated at the last interaction will perform as hints to guide toward the responses of the next interaction. Our experiments show that R$^3$ prompting significantly outperforms existing CoT prompting methods on five reasoning tasks under noisy context. With GPT-3.5-turbo, we observe 3.7% accuracy improvement on average on the reasoning tasks under noisy context compared to the most competitive prompting baseline. More analyses and ablation studies show the robustness and generalization of R$^3$ prompting method in solving reasoning tasks in LLMs under noisy context

Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation

The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. For the sake of expensive cost of large-scale question annotation, the methods of KBQG under low-resource scenarios urgently need to be developed. However, current methods heavily rely on annotated data for fine-tuning, which is not well-suited for few-shot question generation. The emergence of Large Language Models (LLMs) has shown their impressive generalization ability in few-shot tasks. Inspired by Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for reasoning, we formulate KBQG task as a reasoning problem, where the generation of a complete question is splitted into a series of sub-question generation. Our proposed prompting method KQG-CoT first retrieves supportive logical forms from the unlabeled data pool taking account of the characteristics of the logical form. Then, we write a prompt to explicit the reasoning chain of generating complicated questions based on the selected demonstrations. To further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the logical forms by their complexity. We conduct extensive experiments over three public KBQG datasets. The results demonstrate that our prompting method consistently outperforms other prompting baselines on the evaluated datasets. Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4, METEOR, and ROUGE-L, respectively.Comment: Accepted by EMNLP 2023 main conferenc

The Influence of Viewing Photos of Different Types of Rural Landscapes on Stress in Beijing

The environment can affect people’s health by relieving stress, and rural landscape as a special environment might influence human’s stress relief. This study takes different types of rural landscapes as the research object to explore their impact on stress levels, which are shown by photos. As an independent variable, the rural landscape is divided into three levels: Type 1 (natural landscape), type 2 (productive landscape), and type 3 (artificial landscape). Seventy-three subjects were randomly assigned to each type of rural landscape. Salivary cortisol, blood pressure, heart rate, and a subjective rating state scale (brief profile of mood states, BPOMS) were used as indicators of stress. At the same time, the influence of preference and familiarity on the stress relieving effect was also discussed. A paired t-test and one-way analysis of variance (ANOVA) were used as the main statistical methods. In the results of t-test for pre-posttest, significant difference was observed in high blood pressure, heart rate, and total mood disturbance (TMD) of type 1 and type 2, and the high and low blood pressure of type 3; ANOVA analysis revealed that for the difference of pre-posttest, significant difference was observed in the TMD value among the three types; except for type 3, blood pressure, heart rate, and BPOMS values were significantly affected by preference and familiarity. The conclusions include the following: The three types of rural landscapes have a positive effect on relieving stress; the productive landscape has the best effect on relieving stress; and users’ landscape preferences and familiarity with the environment can affect the effect of stress relief in rural landscapes

Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches

International audienceMicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules

Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement.

International audienceArgonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems

The proposed two-step model of miRNA loading into hAgo2: Selective binding followed by structural re-arrangement (highlighted by the cyan arrow).

<p>The induced fit mechanism (marked by the upper right grey arrow) and the conformational selection mechanism (marked by the lower left grey arrow) are also presented to compare with the two-step model. Average transition times between the closed states, the open state and the partially open states are computed from ten independent 10-ms synthetic trajectories generated by sampling the transition probability matrix of the 480-microstate MSM (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004404#pcbi.1004404.s010" target="_blank">S10 Fig</a> for additional details). The detailed transition pathways from the closed states to the open state can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004404#pcbi.1004404.s011" target="_blank">S11 Fig</a>.</p

Visualization of the seven metastable macrostates obtained from MSM of apo hAgo2.

<p>(A) Distribution of the PAZ-PIWI loops center-of-mass (c.o.m.) distances for each macrostate. A large distance implies an open conformation. The equilibrium population of each macrostate is presented. (B) Projections of the open and partially open states onto PAZ-PIWI loops c.o.m. distance and the major PIWI loop angle (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004404#pcbi.1004404.s003" target="_blank">S3 Fig</a> for the angle definition). The green cross corresponds to the binary partially open crystal structure (missing residues modeled). (C) Representative structures of closed, partially open and open states. Enlarged view of the inter-domain region between PAZ (red) and PIWI (green) of each structure is presented in the inset panel (see representative structure for each macrostate in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004404#pcbi.1004404.s004" target="_blank">S4 Fig</a>).</p

Mutations in PIWI loops destabilize the closed conformation and accelerate the closed-to-open transitions.

<p>Three mutants were generated: D823A (green), E821A-D823A-E826A (red) and ∆602–605∆819–833 (a deletion mutant where both PIWI loops are truncated, cyan). MD simulations of WT hAgo2 (blue) and the mutants were initiated from three conformations: (A) a closed conformation, (B) a partially open conformation extracted from the binary hAgo2-miRNA crystal structure (PDB ID: 4F3T) and (C) an open conformation. Time traces of the c.o.m. distance between the PAZ domain and PIWI loops of the WT hAgo2 and the three mutants are displayed. Error bars were computed from five independent MD simulations.</p

Projection of hAgo2-miRNA docking models built from the selected structures of open microstates.

<p>Red dots mark the successful docking models and black dots mark the unsuccessful ones. A successful docking model is a hAgo2-miRNA docking pose where at least two native contacts are preserved at each miRNA terminus.</p