WHAT\textit{WHAT}, WHEN\textit{WHEN}, and HOW\textit{HOW} to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue

This paper presents a method for building a personalized open-domain dialogue system to address the $\textit{WWH}$ ($\textit{WHAT}$, $\textit{WHEN}$, and $\textit{HOW}$) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of $\textit{WWH}$ in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations.Comment: Accepted in ACL 2023 Industry Trac

Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback

Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed-source LLMs (i.e., ChatGPT and GPT-4) are capable of generating corrective feedback to edit erroneous inputs. However, it remains challenging for open-source code LLMs to generate feedback for code editing, since these models tend to adhere to the superficial formats of feedback and provide feedback with misleading information. Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing. To this end, we present Coffee, a collected dataset specifically designed for code fixing with feedback. Using this dataset, we construct CoffeePots, a framework for COde Fixing with FEEdback via Preference-Optimized Tuning and Selection. The proposed framework aims to automatically generate helpful feedback for code editing while minimizing the potential risk of superficial feedback. The combination of Coffee and CoffeePots marks a significant advancement, achieving state-of-the-art performance on HumanEvalFix benchmark. Codes and model checkpoints are publicly available at https://github.com/Lune-Blue/COFFEE.Comment: Work in progres

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents

Human-like chatbots necessitate the use of commonsense reasoning in order to effectively comprehend and respond to implicit information present within conversations. Achieving such coherence and informativeness in responses, however, is a non-trivial task. Even for large language models (LLMs), the task of identifying and aggregating key evidence within a single hop presents a substantial challenge. This complexity arises because such evidence is scattered across multiple turns in a conversation, thus necessitating integration over multiple hops. Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning. To this end, we propose a knowledge distillation framework that leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. We further present DOCTOR, a DialOgue Chain-of-ThOught Reasoner that provides reliable CoT rationales for response generation. We conduct extensive experiments to show that enhancing dialogue agents with high-quality rationales from DOCTOR significantly improves the quality of their responses.Comment: 25 pages, 8 figures, Accepted to EMNLP 202

Stability analysis of f(R)-AdS black holes

We study the stability of f(R)-AdS (Schwarzschild-AdS) black hole obtained from f(R) gravity. In order to resolve the difficulty of solving fourth order linearized equations, we transform f(R) gravity into the scalar-tensor theory by introducing two auxiliary scalars. In this case, the linearized curvature scalar becomes a dynamical scalaron, showing that all linearized equations are second order. Using the positivity of gravitational potentials and S-deformed technique allows us to guarantee the stability of f(R)-AdS black hole if the scalaron mass squared satisfies the Breitenlohner-Freedman bound. This is confirmed by computing quasinormal frequencies of the scalaron for large f(R)-AdS black hole.Comment: 17 pages, 1 figure, version to appear in EPJ

Prediction of Wave Transmission Characteristics of Low-Crested Structures with Comprehensive Analysis of Machine Learning

The adoption of low-crested and submerged structures (LCS) reduces the wave behind a structure, depending on the changes in the freeboard, and induces stable waves in the offshore. We aimed to estimate the wave transmission coefficient behind LCS structures to determine the feasible characteristics of wave mitigation. In addition, various empirical formulas based on regression analysis were proposed to quantitatively predict wave attenuation characteristics for field applications. However, inherent variability of wave attenuation causes the limitation of linear statistical approaches, such as linear regression analysis. Herein, to develop an optimization model for the hydrodynamic behavior of the LCS, we performed a comprehensive analysis of 10 types of machine learning models, which were compared and reviewed on the prediction accuracy with existing empirical formulas. We found that, among the 10 models, the gradient boosting model showed the highest prediction accuracy with MSE of 1.0 × 10−3, an index of agreement of 0.996, a scatter index of 0.065, and a correlation coefficient of 0.983, which indicates a performance improvement over the existing empirical formulas. In addition, based on a variable importance analysis using explainable artificial intelligence, we determined the significant importance of the input variable for the relative freeboard (RC/H0) and the relative freeboard to water depth ratio (RC/h), which confirms that the relative freeboard was the most dominant factor for influencing wave attenuation in the hydraulic behavior around the LCS. Thus, we concluded that the performance prediction method using a machine learning model can be applied to various predictive studies in the field of coastal engineering, deviating from existing empirical-based research

Effect of bimodal surface modification of graphyne on enhanced H2 storage: Density functional theory study

Studies on hydrogen storage materials are proceeding worldwide to enhance adsorptive capacity with a proper adsorption energy between physisorption and chemisorption. Graphyne is the carbon allotropes having sp- and sp2-hybridized carbon atoms. It has not been synthesized yet, but have taken the center stage for decades owing to its promising electronic properties and applicability. Therefore, we performed geometry optimization using density functional theory calculation to determine the adsorptive behaviors of graphyne with decoration and doping approaches for improving hydrogen storage. Graphyne decorated with alkali metal cations showed highly adsorptive properties owing to the enhanced basicity by cations, whereas doped graphyne showed a lower adsorption energy within the desirable hydrogen storage range (−0.20 eV to −0.70 eV). Therefore, we applied bimodal surface modification using doped/co-doped graphyne in the presence of alkali decoration. We found that both decoration and doping approaches compensated each other, yielding an energy suitable for hydrogen storage (min: −0.24 eV, max: −0.32 eV), which elucidates the promising properties of a hydrogen storage material