Search (25 results, page 1 of 2)

  • × theme_ss:"Computerlinguistik"
  • × type_ss:"a"
  • × type_ss:"el"
  • × year_i:[2020 TO 2030}
  1. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.03 
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    Date
    29.12.2022 18:22:55
    Type
    a
  2. Rieger, F.: Lügende Computer (2023) 0.03 
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    Date
    16. 3.2023 19:22:55
    Type
    a
  3. Hausser, R.: Grammatical disambiguation : the linear complexity hypothesis for natural language (2020) 0.00 
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    Abstract
    DBS uses a strictly time-linear derivation order. Therefore the basic computational complexity degree of DBS is linear time. The only way to increase DBS complexity above linear is repeating ambiguity. In natural language, however, repeating ambiguity is prevented by grammatical disambiguation. A classic example of a grammatical ambiguity is the 'garden path' sentence The horse raced by the barn fell. The continuation horse+raced introduces an ambiguity between horse which raced and horse which was raced, leading to two parallel derivation strands up to The horse raced by the barn. Depending on whether the continuation is interpunctuation or a verb, they are grammatically disambiguated, resulting in unambiguous output. A repeated ambiguity occurs in The man who loves the woman who feeds Lucy who Peter loves., with who serving as subject or as object. These readings are grammatically disambiguated by continuing after who with a verb or a noun.
    Type
    a
  4. Roose, K.: ¬The brilliance and weirdness of ChatGPT (2022) 0.00 
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    Abstract
    A new chatbot from OpenAI is inspiring awe, fear, stunts and attempts to circumvent its guardrails.
    Type
    a
  5. Collard, J.; Paiva, V. de; Fong, B.; Subrahmanian, E.: Extracting mathematical concepts from text (2022) 0.00 
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    Abstract
    We investigate different systems for extracting mathematical entities from English texts in the mathematical field of category theory as a first step for constructing a mathematical knowledge graph. We consider four different term extractors and compare their results. This small experiment showcases some of the issues with the construction and evaluation of terms extracted from noisy domain text. We also make available two open corpora in research mathematics, in particular in category theory: a small corpus of 755 abstracts from the journal TAC (3188 sentences), and a larger corpus from the nLab community wiki (15,000 sentences).
    Type
    a
  6. Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020) 0.00 
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    Abstract
    Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
    Type
    a
  7. Park, J.S.; O'Brien, J.C.; Cai, C.J.; Ringel Morris, M.; Liang, P.; Bernstein, M.S.: Generative agents : interactive simulacra of human behavior (2023) 0.00 
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    Abstract
    Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.
    Type
    a
  8. Metz, C.: ¬The new chatbots could change the world : can you trust them? (2022) 0.00 
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  9. Simanowski, R.: Wenn die Dinge anfangen zu sprechen : Chatbot LaMDA von Google (2022) 0.00 
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  10. Leighton, T.: ChatGPT und Künstliche Intelligenz : Utopie oder Dystopie? (2023) 0.00 
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  11. Barthel, J.; Ciesielski, R.: Regeln zu ChatGPT an Unis oft unklar : KI in der Bildung (2023) 0.00 
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  12. Janssen, J.-K.: ChatGPT-Klon läuft lokal auf jedem Rechner : Alpaca/LLaMA ausprobiert (2023) 0.00 
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  13. Lutz-Westphal, B.: ChatGPT und der "Faktor Mensch" im schulischen Mathematikunterricht (2023) 0.00 
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  14. Harari, Y.N.: ¬[Yuval-Noah-Harari-argues-that] AI has hacked the operating system of human civilisation (2023) 0.00 
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  15. Hahn, S.: DarkBERT ist mit Daten aus dem Darknet trainiert : ChatGPTs dunkler Bruder? (2023) 0.00 
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  16. Siepmann, D.: Auswirkungen von KI auf die Textproduktion in der Wissenschaft (2023) 0.00 
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  17. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023) 0.00 
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  18. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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  19. Shree, P.: ¬The journey of Open AI GPT models (2020) 0.00 
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    Abstract
    Generative Pre-trained Transformer (GPT) models by OpenAI have taken natural language processing (NLP) community by storm by introducing very powerful language models. These models can perform various NLP tasks like question answering, textual entailment, text summarisation etc. without any supervised training. These language models need very few to no examples to understand the tasks and perform equivalent or even better than the state-of-the-art models trained in supervised fashion. In this article we will cover the journey of these models and understand how they have evolved over a period of 2 years. 1. Discussion of GPT-1 paper (Improving Language Understanding by Generative Pre-training). 2. Discussion of GPT-2 paper (Language Models are unsupervised multitask learners) and its subsequent improvements over GPT-1. 3. Discussion of GPT-3 paper (Language models are few shot learners) and the improvements which have made it one of the most powerful models NLP has seen till date. This article assumes familiarity with the basics of NLP terminologies and transformer architecture.
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    a
  20. Kurz, C.: Womit sich Strafverfolger bald befassen müssen : ChatGPT (2023) 0.00 
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    Type
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Authors

Languages

  • d 16
  • e 9