Overall, our work offers a novel perspective on the cerebellum as a brainneuronal observations while making several testable predictions across multiple mental observations. Moreover, our model also explains recent behavioural and learning while reducing ataxia-like behaviours, consistent with classical experishown to be cerebellar-dependent. In all tasks, we observe that ccRNNs facilitates and cognitive tasks (pattern recognition and caption generation) that have been network (ccRNN) model on a number of sensorimotor (line and digit drawing) tions from a cerebellar module. We test this cortico-cerebellar recurrent neural in which a recurrent cortical network receives online temporal feedback predicdemonstrate the potential of this framework we introduce a systems-level model lum, helps the cerebral cortex solve similar locking problems akin to DNIs.

Traditional approaches to network management have been accessible only to a handful of highly-trained network operators with significant expert knowledge. This creates barriers for lay users to easily manage their networks without resorting to experts. With recent development of powerful large language models (LLMs) for language comprehension, we design a system to make network management accessible to a broader audience of non-experts by allowing users to converse with networks in natural language. To effectively leverage advancements in LLMs, we propose an agentic framework that uses an intermediate representation to streamline configuration across diverse vendor equipment, retrieves the network state from memory in real-time, and provides an interface for external feedback. We also conduct pilot studies to collect real user data of natural language utterances for network control, and present a visualization interface to facilitate dialogue-driven user interaction and enable large-scale data collection for future development. Preliminary experiments validate the effectiveness of our proposed system components with LLM integration on both synthetic and real user utterances. Through our data collection and visualization efforts, we pave the way for more effective use of LLMs and democratize network control for everyday users.

Self-reflection for Large Language Models (LLMs) has gained significant attention. Existing approaches involve models iterating and improving their previous responses based on LLMs' internal reflection ability or external feedback. However, recent research has raised doubts about whether intrinsic self-correction without external feedback may even degrade performance. Based on our empirical evidence, we find that current static reflection methods may lead to redundant, drift, and stubborn issues. To mitigate this, we introduce Instruct-of-Reflection (IoRT), a novel and general reflection framework that leverages dynamic-meta instruction to enhance the iterative reflection capability of LLMs. Specifically, we propose the instructor driven by the meta-thoughts and self-consistency classifier, generates various instructions, including refresh, stop, and select, to guide the next reflection iteration. Our experiments demonstrate that IoRT achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability.

Though intensive attentions to the self-correction capability of Large Language Models (LLMs), the underlying mechanism of this capability is still under-explored. In this paper, we aim to answer two fundamental questions for moral self-correction: (1) how different components in self-correction, such as Chain-of-Thought (CoT) reasoning, external feedback, and instructional prompts, interact to enable moral self-correction; and (2) is the self-correction one of LLMs' innate capabilities? To answer the first question, we examine how different self-correction components interact to intervene the embedded morality within hidden states, therefore contributing to different performance. For the second question, we (i) evaluate the robustness of moral self-correction by introducing natural language interventions of weak evidence into prompts; (ii) propose a validation framework, self-distinguish, that requires effective self-correction to enable LLMs to distinguish between desirable and undesirable outputs. Our experimental results indicate that there is no universally optimal self-correction method for the tasks considered, although external feedback and CoT can contribute to additional performance gains. However, our mechanistic analysis reveals negative interactions among instructional prompts, CoT, and external feedback, suggesting a conflict between internal knowledge and external feedback. The self-distinguish experiments demonstrate that while LLMs can self-correct their responses, they are unable to reliably distinguish between desired and undesired outputs. With our empirical evidence, we can conclude that moral self-correction is not an innate capability of LLMs acquired during pretraining.

While Vision-Language Models (VLMs) have shown remarkable abilities in visual and language reasoning tasks, they invariably generate flawed responses. Self-correction that instructs models to refine their outputs presents a promising solution to this issue. Previous studies have mainly concentrated on Large Language Models (LLMs), while the self-correction abilities of VLMs, particularly concerning both visual and linguistic information, remain largely unexamined. This study investigates the self-correction capabilities of VLMs during both inference and fine-tuning stages. We introduce a Self-Correction Learning (SCL) approach that enables VLMs to learn from their self-generated self-correction data through Direct Preference Optimization (DPO) without relying on external feedback, facilitating self-improvement. Specifically, we collect preferred and disfavored samples based on the correctness of initial and refined responses, which are obtained by two-turn self-correction with VLMs during the inference stage. Experimental results demonstrate that although VLMs struggle to self-correct effectively during iterative inference without additional fine-tuning and external feedback, they can enhance their performance and avoid previous mistakes through preference fine-tuning when their self-generated self-correction data are categorized into preferred and disfavored samples. This study emphasizes that self-correction is not merely a refinement process; rather, it should enhance the reasoning abilities of models through additional training, enabling them to generate high-quality responses directly without further refinement.

While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents. We introduce LATS (Language Agent Tree Search), a general framework that synergizes the capabilities of LLMs in planning, acting, and reasoning. Drawing inspiration from Monte Carlo tree search in model-based reinforcement learning, LATS employs LLMs as agents, value functions, and optimizers, repurposing their latent strengths for enhanced decision-making. What is crucial in this method is the use of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that moves beyond the limitations of existing techniques. Our experimental evaluation across diverse domains, such as programming, HotPotQA, and WebShop, illustrates the applicability of LATS for both reasoning and acting. In particular, LATS achieves 94.4% for programming on HumanEval with GPT-4 and an average score of 75.9 for web browsing on WebShop with GPT-3.5, demonstrating the effectiveness and generality of our method.

Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues. Building upon this premise, this paper critically examines the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback. In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance might even degrade post self-correction. Drawing from these insights, we offer suggestions for future research and practical applications in this field.

"I'm trying to go down a bottomless pit. I'll never make it till the end." That's what I thought when I tried to create my own video game. I was young, beautiful, and I was struggling to use for loops and arrays at the same time. There was so much to learn! Fortunately, I found the strength to continue. More and more, the concepts behind programming began to make sense. Going through a book about C and trying to create my own adventure on MS-DOS was a crazy Indiana Jone's-like discovery I'll never forget. My first video game wasn't great, but it was mine. Yet, what I remember today, with a tear in my left eye, is not the result, but the learning process itself. It was these "Aha!" moments which brought me the most joy! That's why I tried, through the years, to make my learning gradually more effective and efficient.