Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Baumgärtner, Tim"'
Segmenting text into fine-grained units of meaning is important to a wide range of NLP applications. The default approach of segmenting text into sentences is often insufficient, especially since sentences are usually complex enough to include multip
Externí odkaz:
http://arxiv.org/abs/2406.19803
Reinforcement Learning from Human Feedback (RLHF) is a popular method for aligning Language Models (LM) with human values and preferences. RLHF requires a large number of preference pairs as training data, which are often used in both the Supervised
Externí odkaz:
http://arxiv.org/abs/2404.05530
Autor:
Puerto, Haritz, Baumgärtner, Tim, Sachdeva, Rachneet, Fang, Haishuo, Zhang, Hao, Tariverdian, Sewin, Wang, Kexin, Gurevych, Iryna
The continuous development of Question Answering (QA) datasets has drawn the research community's attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their re
Externí odkaz:
http://arxiv.org/abs/2303.18120
Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like question answering or navigational queries, however, for information-se
Externí odkaz:
http://arxiv.org/abs/2210.10695
Autor:
Sachdeva, Rachneet, Puerto, Haritz, Baumgärtner, Tim, Tariverdian, Sewin, Zhang, Hao, Wang, Kexin, Saadi, Hossain Shaikh, Ribeiro, Leonardo F. R., Gurevych, Iryna
Question Answering (QA) systems are increasingly deployed in applications where they support real-world decisions. However, state-of-the-art models rely on deep neural networks, which are difficult to interpret by humans. Inherently interpretable mod
Externí odkaz:
http://arxiv.org/abs/2208.09316
Autor:
Baumgärtner, Tim, Wang, Kexin, Sachdeva, Rachneet, Eichler, Max, Geigle, Gregor, Poth, Clifton, Sterz, Hannah, Puerto, Haritz, Ribeiro, Leonardo F. R., Pfeiffer, Jonas, Reimers, Nils, Şahin, Gözde Gül, Gurevych, Iryna
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require different model architectures (e.g., generative, discriminative), and
Externí odkaz:
http://arxiv.org/abs/2203.13693
Autor:
Baan, Joris, Leible, Jana, Nikolaus, Mitja, Rau, David, Ulmer, Dennis, Baumgärtner, Tim, Hupkes, Dieuwke, Bruni, Elia
We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task. The models are architecturally identical at inference time, but differ in the way that they are trained: our baseline model is train
Externí odkaz:
http://arxiv.org/abs/1906.01634
Autor:
Haber, Janosch, Baumgärtner, Tim, Takmaz, Ece, Gelderloos, Lieke, Bruni, Elia, Fernández, Raquel
This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on d
Externí odkaz:
http://arxiv.org/abs/1906.01530
Autor:
Shekhar, Ravi, Venkatesh, Aashish, Baumgärtner, Tim, Bruni, Elia, Plank, Barbara, Bernardi, Raffaella, Fernández, Raquel
We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify a
Externí odkaz:
http://arxiv.org/abs/1809.03408
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