562,592 research outputs found

    Lemur: Harmonizing Natural Language and Code for Language Agents

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    We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemu

    Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge

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    Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms. However, the dimensions of dense embedding spaces are not designed to resemble human semantic knowledge. Moreover, embeddings are often built from a single source of information (typically text data), even though neurocognitive research suggests that semantics is deeply linked to both language and perception. In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding. Through in-depth analyses comparing these sparse models to human-derived behavioural and neuroimaging data, we demonstrate their ability to predict interpretable linguistic descriptions of human ground-truth semantic knowledge.Comment: Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 260-270. Brussels, Belgium, October 31 - November 1, 2018. Association for Computational Linguistic

    Auto-Detection of Programming Code Vulnerabilities With Natural Language Processing

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    Security vulnerabilities in source code are traditionally detected manually by software developers because there are no effective auto-detection tools. Current vulnerability detection tools require great human effort, and the results have flaws in many ways. However, deep learning models could be a solution to this problem for the following reasons: 1. Deep learning models are relatively accurate for text classification and text summarization for source code. 2. After being deployed on the cloud servers, the efficiency of deep learning based auto-detection could be much higher than human effort. Therefore, we developed two Natural Language Processing(NLP) models: the first one is a text-classification model that takes source code as input and outputs the classification of the security vulnerability of the input. The second one is a text-to-text model that takes source code as input and outputs a completely machine-generated summary about the security vulnerability of the input. Our evaluation shows that both models get impressive results

    Text Segmentation Using Exponential Models

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    This paper introduces a new statistical approach to partitioning text automatically into coherent segments. Our approach enlists both short-range and long-range language models to help it sniff out likely sites of topic changes in text. To aid its search, the system consults a set of simple lexical hints it has learned to associate with the presence of boundaries through inspection of a large corpus of annotated data. We also propose a new probabilistically motivated error metric for use by the natural language processing and information retrieval communities, intended to supersede precision and recall for appraising segmentation algorithms. Qualitative assessment of our algorithm as well as evaluation using this new metric demonstrate the effectiveness of our approach in two very different domains, Wall Street Journal articles and the TDT Corpus, a collection of newswire articles and broadcast news transcripts.Comment: 12 pages, LaTeX source and postscript figures for EMNLP-2 pape
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