The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding. This paper introduces PointLLM, a preliminary effort to fill this gap, enabling LLMs to understand point clouds and offering a new avenue beyond 2D visual data. PointLLM understands colored object point clouds with human instructions and generates contextually appropriate responses, illustrating its grasp of point clouds and common sense. Specifically, it leverages a point cloud encoder with a powerful LLM to effectively fuse geometric, appearance, and linguistic information. We collect a novel dataset comprising 660K simple and 70K complex point-text instruction pairs to enable a two-stage training strategy: aligning latent spaces and subsequently instruction-tuning the unified model. To rigorously evaluate the perceptual and generalization capabilities of PointLLM, we establish two benchmarks: Generative 3D Object Classification and 3D Object Captioning, assessed through three different methods, including human evaluation, GPT-4/ChatGPT evaluation, and traditional metrics. Experimental results reveal PointLLM's superior performance over existing 2D and 3D baselines, with a notable achievement in human-evaluated object captioning tasks where it surpasses human annotators in over 50% of the samples. Codes, datasets, and benchmarks are available at https://github.com/OpenRobotLab/PointLLM .

While language models are powerful and versatile, they often fail to address highly complex problems. This is because solving complex problems requires deliberate thinking, which has been only minimally guided during training. In this paper, we propose a new method called Cumulative Reasoning (CR), which employs language models in a cumulative and iterative manner to emulate human thought processes. By decomposing tasks into smaller components, CR streamlines the problem-solving process, rendering it both more manageable and effective. For logical inference tasks, CR consistently outperforms existing methods with an improvement up to 9.3%, and achieves an accuracy of 98.04% on the curated FOLIO wiki dataset. In the context of the Game of 24, CR achieves an accuracy of 98%, which signifies a substantial enhancement of 24% over the previous state-of-the-art method. Finally, on the MATH dataset, we establish new state-of-the-art results with 58.0% overall accuracy, surpassing the previous best approach by a margin of 4.2%, and achieving 43% relative improvement on the hardest level 5 problems (22.4% to 32.1%). Additionally, we expand the concept of Cumulative Reasoning to incorporate a Python code environment, deliberately omitting external aids such as retrieval and web browsing and focusing solely on the LLM's intrinsic reasoning capabilities within a Python code environment. Our experiments in this setting yielded impressive results, with an overall accuracy of 72.2% on the MATH dataset, significantly outperforming the PAL method with 38.8% relative improvement. Code is available at https://github.com/iiis-ai/cumulative-reasoning.

To what extent can language alone give rise to complex concepts, or is embodied experience essential? Recent advancements in large language models (LLMs) offer fresh perspectives on this question. Although LLMs are trained on restricted modalities, they exhibit human-like performance in diverse psychological tasks. Our study compared representations of 4,442 lexical concepts between humans and ChatGPTs (GPT-3.5 and GPT-4) across multiple dimensions, including five key domains: emotion, salience, mental visualization, sensory, and motor experience. We identify two main findings: 1) Both models strongly align with human representations in non-sensorimotor domains but lag in sensory and motor areas, with GPT-4 outperforming GPT-3.5; 2) GPT-4's gains are associated with its additional visual learning, which also appears to benefit related dimensions like haptics and imageability. These results highlight the limitations of language in isolation, and that the integration of diverse modalities of inputs leads to a more human-like conceptual representation.

GPT-2 [40], GPT-3 [4] and GPT-Neo [3] have been shown to generate high-quality texts for these tasks. To generate a sequence of A key component of generating text from modern language models tokens, LMs produce a probability distribution over the vocabulary (LM) is the selection and tuning of decoding algorithms. These algorithms at each time step, from which the predicted token is drawn. Enumerating determine how to generate text from the internal probability all possible output sequences for a given input and choosing distribution generated by the LM. The process of choosing a decoding the one with the highest probability is intractable; furthermore, algorithm and tuning its hyperparameters takes significant relatively low-probability sequences may even be desirable for certain time, manual effort, and computation, and it also requires extensive tasks (e.g., creative writing). Therefore, LMs rely on decoding human evaluation. Therefore, the identity and hyperparameters of algorithms to decide which output tokens to produce based on their such decoding algorithms are considered to be extremely valuable probabilities, i.e., to decode the text.

This paper elucidates the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics, drawing inspiration from the success of materials informatics. Highlighting the intricacies of soil complexity, heterogeneity, and the lack of comprehensive data, the discussion underscores the pressing need for community-driven database initiatives and open science movements. By leveraging the transformative power of deep learning, particularly in feature extraction from high-dimensional data and the potential of transfer learning, we envision a paradigm shift towards a more collaborative and innovative geotechnics field. The paper concludes with a forward-looking stance, emphasizing the revolutionary potential brought about by advanced computational tools like large language models in reshaping geotechnics informatics.

With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focuses on the Post-Training Quantization (PTQ) of LLMs. Drawing from recent advances, our work introduces QuantEase, a layer-wise quantization framework where individual layers undergo separate quantization. The problem is framed as a discrete-structured non-convex optimization, prompting the development of algorithms rooted in Coordinate Descent (CD) techniques. These CD-based methods provide high-quality solutions to the complex non-convex layer-wise quantization problems. Notably, our CD-based approach features straightforward updates, relying solely on matrix and vector operations, circumventing the need for matrix inversion or decomposition. We also explore an outlier-aware variant of our approach, allowing for retaining significant weights (outliers) with complete precision. Our proposal attains state-of-the-art performance in terms of perplexity and zero-shot accuracy in empirical evaluations across various LLMs and datasets, with relative improvements up to 15% over methods such as GPTQ. Leveraging careful linear algebra optimizations, QuantEase can quantize models like Falcon-180B on a single NVIDIA A100 GPU in $\sim$3 hours. Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity.

Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially serious consequences. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsible and effective deployment in real-world applications. Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) to address this problem, where generative models are fine-tuned with RL algorithms guided by a human-feedback-informed reward model. However, the inefficiencies and instabilities associated with RL algorithms frequently present substantial obstacles to the successful alignment, necessitating the development of a more robust and streamlined approach. To this end, we introduce a new framework, Reward rAnked FineTuning (RAFT), designed to align generative models effectively. Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples. Our studies show that RAFT can effectively improve the model performance in both reward learning and other automated metrics in both large language models and diffusion models.

Last week, after briefly deposed CEO Sam Altman was reinstalled at OpenAI, two reports claimed that a top-secret project at the company had rattled some researchers there with its potential to solve intractable problems in a powerful new way. "Given vast computing resources, the new model was able to solve certain mathematical problems," Reuters reported, citing a single unnamed source. "Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*'s future success." The Information said that Q* was seen as a breakthrough that would lead to "far more powerful artificial intelligence models," adding that "the pace of development alarmed some researchers focused on AI safety," citing a single unnamed source. Reuters also reported that some researchers sent a letter expressing concerns about Q*'s potential power to the nonprofit board that ejected Altman, although a WIRED source familiar with the board's thinking says that was not the case.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. City lawmakers in Brazil have enacted what appears to be the nation's first legislation written entirely by artificial intelligence -- even if they didn't know it at the time. The experimental ordinance was passed in October in the southern city of Porto Alegre and city councilman Ramiro Rosário revealed this week that it was written by a chatbot, sparking objections and raising questions about the role of artificial intelligence in public policy. Rosário told The Associated Press that he asked OpenAI's chatbot ChatGPT to craft a proposal to prevent the city from charging taxpayers to replace water consumption meters if they are stolen.

ChatGPT is one year old today, and it's accomplished a lot in its first trip around the sun. The chatbot has upended or outright killed high-school and college essay writing and thoroughly scrambled the brains of academics, creating an on-campus arms race that professors have already lost. It has been used to write books, article summaries, and political content, and it has flooded online marketplaces with computer-generated slop. As we've gotten to know ChatGPT, we've noticed how malleable it is. The li'l bot loves clichés.