Serving disaggregated large language models (LLMs) over tens of thousands of xPU devices (GPUs or NPUs) with reliable performance faces multiple challenges. 1) Ignoring the diversity (various prefixes and tidal requests), treating all the prompts in a mixed pool is inadequate. To facilitate the similarity per scenario and minimize the inner mismatch on P/D (prefill and decoding) processing, fine-grained organization is required, dynamically adjusting P/D ratios for better performance. 2) Due to inaccurate estimation on workload (queue status or maintained connections), the global scheduler easily incurs unnecessary timeouts in prefill. 3) Block-fixed device-to-device (D2D) KVCache transfer over cluster-level RDMA (remote direct memory access) fails to achieve desired D2D utilization as expected. To overcome previous problems, this paper proposes an end-to-end system P/D-Serve, complying with the paradigm of MLOps (machine learning operations), which models end-to-end (E2E) P/D performance and enables: 1) fine-grained P/D organization, mapping the service with RoCE (RDMA over converged ethernet) as needed, to facilitate similar processing and dynamic adjustments on P/D ratios; 2) on-demand forwarding upon rejections for idle prefill, decoupling the scheduler from regular inaccurate reports and local queues, to avoid timeouts in prefill; and 3) efficient KVCache transfer via optimized D2D access. P/D-Serve is implemented upon Ascend and MindSpore, has been deployed over tens of thousands of NPUs for more than eight months in commercial use, and further achieves 60\%, 42\% and 46\% improvements on E2E throughput, time-to-first-token (TTFT) SLO (service level objective) and D2D transfer time. As the E2E system with optimizations, P/D-Serve achieves 6.7x increase on throughput, compared with aggregated LLMs.

The rapid advancements in Large Language Models (LLMs) have led to significant improvements in various natural language processing tasks. However, the evaluation of LLMs' legal knowledge, particularly in non-English languages such as Arabic, remains under-explored. To address this gap, we introduce ArabLegalEval, a multitask benchmark dataset for assessing the Arabic legal knowledge of LLMs. Inspired by the MMLU and LegalBench datasets, ArabLegalEval consists of multiple tasks sourced from Saudi legal documents and synthesized questions. In this work, we aim to analyze the capabilities required to solve legal problems in Arabic and benchmark the performance of state-of-the-art LLMs. We explore the impact of in-context learning and investigate various evaluation methods. Additionally, we explore workflows for generating questions with automatic validation to enhance the dataset's quality. We benchmark multilingual and Arabic-centric LLMs, such as GPT-4 and Jais, respectively. We also share our methodology for creating the dataset and validation, which can be generalized to other domains. We hope to accelerate AI research in the Arabic Legal domain by releasing the ArabLegalEval dataset and code: https://github.com/Thiqah/ArabLegalEval

The conventional BIM authoring process typically requires designers to master complex and tedious modeling commands in order to materialize their design intentions within BIM authoring tools. This additional cognitive burden complicates the design process and hinders the adoption of BIM and model-based design in the AEC (Architecture, Engineering, and Construction) industry. To facilitate the expression of design intentions more intuitively, we propose Text2BIM, an LLM-based multi-agent framework that can generate 3D building models from natural language instructions. This framework orchestrates multiple LLM agents to collaborate and reason, transforming textual user input into imperative code that invokes the BIM authoring tool's APIs, thereby generating editable BIM models with internal layouts, external envelopes, and semantic information directly in the software. Furthermore, a rule-based model checker is introduced into the agentic workflow, utilizing predefined domain knowledge to guide the LLM agents in resolving issues within the generated models and iteratively improving model quality. Extensive experiments were conducted to compare and analyze the performance of three different LLMs under the proposed framework. The evaluation results demonstrate that our approach can effectively generate high-quality, structurally rational building models that are aligned with the abstract concepts specified by user input. Finally, an interactive software prototype was developed to integrate the framework into the BIM authoring software Vectorworks, showcasing the potential of modeling by chatting.

Automated fact-checking is an important task because determining the accurate status of a proposed claim within the vast amount of information available online is a critical challenge. This challenge requires robust evaluation to prevent the spread of false information. Modern large language models (LLMs) have demonstrated high capability in performing a diverse range of Natural Language Processing (NLP) tasks. By utilizing proper prompting strategies, their versatility due to their understanding of large context sizes and zero-shot learning ability enables them to simulate human problem-solving intuition and move towards being an alternative to humans for solving problems. In this work, we introduce a straightforward framework based on Zero-Shot Learning and Key Points (ZSL-KeP) for automated fact-checking, which despite its simplicity, performed well on the AVeriTeC shared task dataset by robustly improving the baseline and achieving 10th place.

Planning in code is considered a more reliable approach for many orchestration tasks. This is because code is more tractable than steps generated via Natural Language and make it easy to support more complex sequences by abstracting deterministic logic into functions. It also allows spotting issues with incorrect function names with the help of parsing checks that can be run on code. Progress in Code Generation methodologies, however, remains limited to general-purpose languages like C, C++, and Python. LLMs continue to face challenges with custom function names in Domain Specific Languages or DSLs, leading to higher hallucination rates and syntax errors. This is more common for custom function names, that are typically part of the plan. Moreover, keeping LLMs up-to-date with newer function names is an issue. This poses a challenge for scenarios like task planning over a large number of APIs, since the plan is represented as a DSL having custom API names. In this paper, we focus on workflow automation in RPA (Robotic Process Automation) domain as a special case of task planning. We present optimizations for using Retrieval Augmented Generation (or RAG) with LLMs for DSL generation along with an ablation study comparing these strategies with a fine-tuned model. Our results showed that the fine-tuned model scored the best on code similarity metric. However, with our optimizations, RAG approach is able to match the quality for in-domain API names in the test set. Additionally, it offers significant advantage for out-of-domain or unseen API names, outperforming Fine-Tuned model on similarity metric by 7 pts.

This paper investigates the task of the open-ended interactive robotic manipulation on table-top scenarios. While recent Large Language Models (LLMs) enhance robots' comprehension of user instructions, their lack of visual grounding constrains their ability to physically interact with the environment. This is because the robot needs to locate the target object for manipulation within the physical workspace. To this end, we introduce an interactive robotic manipulation framework called Polaris, which integrates perception and interaction by utilizing GPT-4 alongside grounded vision models. For precise manipulation, it is essential that such grounded vision models produce detailed object pose for the target object, rather than merely identifying pixels belonging to them in the image. Consequently, we propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline. This pipeline utilizes rendered synthetic data for training and is then transferred to real-world manipulation tasks. The real-world performance demonstrates the efficacy of our proposed pipeline and underscores its potential for extension to more general categories. Moreover, real-robot experiments have showcased the impressive performance of our framework in grasping and executing multiple manipulation tasks. This indicates its potential to generalize to scenarios beyond the tabletop. More information and video results are available here: https://star-uu-wang.github.io/Polaris/

Sequential recommendation systems fundamentally rely on users' historical interaction sequences, which are often contaminated by noisy interactions. Identifying these noisy interactions accurately without additional information is particularly difficult due to the lack of explicit supervisory signals to denote noise. Large Language Models (LLMs), equipped with extensive open knowledge and semantic reasoning abilities, present a promising avenue to bridge this information gap. However, employing LLMs for denoising in sequential recommendation introduces notable challenges: 1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; 2) Even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the task and th inherent hallucinatory issue of LLMs. To tackle these challenges, we propose LLM4DSR, a tailored approach for denoising sequential recommendation using LLMs. We constructed a self-supervised fine-tuning task to activate LLMs' capabilities to identify noisy items and suggest replacements. Furthermore, we developed an uncertainty estimation module that ensures only high-confidence responses are utilized for sequence corrections. Remarkably, LLM4DSR is model-agnostic, allowing the corrected sequences to be flexibly applied across various recommendation models. Extensive experiments validate the superiority of LLM4DSR over existing methods across three datasets and three recommendation backbones.

In knowledge-intensive tasks such as open-domain question answering To overcome the limitations of LLMs' parametric knowledge, retrieval (OpenQA), Large Language Models (LLMs) often struggle augmented generation (RAG) [11, 27] is explored, equipping to generate factual answers relying solely on their internal (parametric) LLMs with a retriever to gather necessary evidence from external knowledge. To address this limitation, Retrieval-Augmented sources. Among the two components of RAG, improving the retriever Generation (RAG) systems enhance LLMs by retrieving relevant information is more feasible due to the recent trend of black-box APIs from external sources, thereby positioning the retriever [33] and the high cost and time requirements of fine-tuning opensource as a pivotal component. Although dense retrieval demonstrates LLMs [10]. The retriever, a critical part of RAG, is typically state-of-the-art performance, its training poses challenges due to either a traditional unsupervised retriever like BM25 [38] or a more the scarcity of ground-truth evidence, largely attributed to the high advanced neural retriever, such as dense retrieval [20, 21, 32, 51], costs of human annotation. In this paper, we propose W-RAG by which encodes questions and passages into the same embedding utilizing the ranking capabilities of LLMs to create weakly labeled space and then measures the question-passage relevance score by data for training dense retrievers.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Long-horizon planning is hindered by challenges such as uncertainty accumulation, computational complexity, delayed rewards and incomplete information. This work proposes an approach to exploit the task hierarchy from human instructions to facilitate multi-robot planning. Using Large Language Models (LLMs), we propose a two-step approach to translate multi-sentence instructions into a structured language, Hierarchical Linear Temporal Logic (LTL), which serves as a formal representation for planning. Initially, LLMs transform the instructions into a hierarchical representation defined as Hierarchical Task Tree, capturing the logical and temporal relations among tasks. Following this, a domain-specific fine-tuning of LLM translates sub-tasks of each task into flat LTL formulas, aggregating them to form hierarchical LTL specifications. These specifications are then leveraged for planning using off-the-shelf planners. Our framework not only bridges the gap between instructions and algorithmic planning but also showcases the potential of LLMs in harnessing hierarchical reasoning to automate multi-robot task planning. Through evaluations in both simulation and real-world experiments involving human participants, we demonstrate that our method can handle more complex instructions compared to existing methods. The results indicate that our approach achieves higher success rates and lower costs in multi-robot task allocation and plan generation. Demos videos are available at https://youtu.be/7WOrDKxIMIs .