ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Large Language Models (LLMs) still struggle with complex reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose ReConcile, a multi-model multi-agent framework designed as a round table conference among diverse LLM agents to foster diverse thoughts and discussion for improved consensus. ReConcile enhances the reasoning capabilities of LLMs by holding multiple rounds of discussion, learning to convince other agents to improve their answers, and employing a confidence-weighted voting mechanism. In each round, ReConcile initiates discussion between agents via a 'discussion prompt' that consists of (a) grouped answers and explanations generated by each agent in the previous round, (b) their uncertainties, and (c) demonstrations of answer-rectifying human explanations, used for convincing other agents. This discussion prompt enables each agent to revise their responses in light of insights from other agents. Once a consensus is reached and the discussion ends, ReConcile determines the final answer by leveraging the confidence of each agent in a weighted voting scheme. We implement ReConcile with ChatGPT, Bard, and Claude2 as the three agents. Our experimental results on various benchmarks demonstrate that ReConcile significantly enhances the reasoning performance of the agents (both individually and as a team), surpassing prior single-agent and multi-agent baselines by 7.7% and also outperforming GPT-4 on some of these datasets. We also experiment with GPT-4 itself as one of the agents in ReConcile and demonstrate that its initial performance also improves by absolute 10.0% through discussion and feedback from other agents. Finally, we also analyze the accuracy after every round and observe that ReConcile achieves better and faster consensus between agents, compared to a multi-agent debate baseline. Our code is available at: https://github.com/dinobby/ReConcileComment: 19 pages, 9 figures, 7 table

Chapter 38 Learning Analytics

In this chapter, we present an overview of the field by articulating definitions and existing models of learning analytics. Case examples of learning analytics from Asian researchers are then summarized and reported. This is followed by an exploration of the key tensions in this field. The chapter concludes with a discussion of potential areas for future research in this area

Pseudo-solid-state electrolytes utilizing the ionic liquid family for rechargeable batteries

The advent of solid-state electrolytes has unearthed a new paradigm of next-generation batteries endowed with improved electrochemical properties and exceptional safety. Amongst them, Li-stuffed garnet type oxides, sulfides, and NASICON type solid-state electrolytes have emerged with fascinating ionic conductivity, electrochemical stability, and high safety standards, besides creating an avenue for using metal anodes to maximize energy density. However, the actual performance of solid-state electrolytes is heavily encumbered by unexpected metal dendrite formation and typically manifests high resistances between the metal electrodes/solid-state electrolytes or grain boundaries, thereby restricting their practical applications. Recent studies have reported several novel approaches, such as modifying solid-state electrolytes using ionic liquids to form the so-called “pseudo-solid-state electrolytes”. This class of electrolytes encompassing materials such as ionogel using ionic liquids and ionic plastic crystals has been gaining rekindled interest for their unique properties that promise great strides in battery performance and diversified utility. This minireview paper summarizes recent progress in pseudo-solid-state electrolytes utilizing ionic liquids, highlighting their fundamental properties while elaborating expedient design strategies. The realistic prospects and future challenges associated with developing pseudo-solid-state electrolyte materials present an insight into their properties to inspire far-reaching exploration into their material characteristics and functionalities

Potassium single cation ionic liquid electrolyte for potassium-ion batteries

Potassium-ion batteries (PIBs) are a promising post-lithium-ion battery (LIB), as their resources are abundant and low-cost and may have a higher voltage than LIBs. However, the high operating voltage and extremely high reactivity of potassium metal require a chemically safe electrolyte with oxidative and reductive stabilities. In this study, a potassium single cation ionic liquid (K-SCIL), which contains only K⁺ as the cationic species and has a high electrochemical stability, low flammability, and low vapor pressure, is developed as an electrolyte for PIBs. The mixture of potassium bis(fluorosulfonyl)amide and potassium (fluorosulfonyl)(trifluoromethylsulfonyl)amide at a molar ratio of 55:45 had the lowest melting point of 67 °C. The K⁺ concentration in this K-SCIL is high (8.5 mol dm⁻³ at 90 °C) due to the absence of solvents and bulky organic cations. In addition, the electrochemical window is as wide as 5.6 V, which enables the construction of PIBs with a high energy density. A high current density can be achieved with this K-SCIL, owing to the absence of a K⁺ concentration gradient. The electrolyte was successfully used with a graphite negative electrode, enabling the reversible intercalation/deintercalation of K⁺, as confirmed by X-ray diffraction