Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Hofmann, Valentin"'
Autor:
Lin, Fangru, Mao, Shaoguang, La Malfa, Emanuele, Hofmann, Valentin, de Wynter, Adrian, Yao, Jing, Chen, Si-Qing, Wooldridge, Michael, Wei, Furu
Language is not monolithic. While many benchmarks are used as proxies to systematically estimate Large Language Models' (LLM) performance in real-life tasks, they tend to ignore the nuances of within-language variation and thus fail to model the expe
Externí odkaz:
http://arxiv.org/abs/2410.11005
Hundreds of millions of people now interact with language models, with uses ranging from serving as a writing aid to informing hiring decisions. Yet these language models are known to perpetuate systematic racial prejudices, making their judgments bi
Externí odkaz:
http://arxiv.org/abs/2403.00742
Autor:
Röttger, Paul, Hofmann, Valentin, Pyatkin, Valentina, Hinck, Musashi, Kirk, Hannah Rose, Schütze, Hinrich, Hovy, Dirk
Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased LL
Externí odkaz:
http://arxiv.org/abs/2402.16786
Autor:
Lin, Fangru, La Malfa, Emanuele, Hofmann, Valentin, Yang, Elle Michelle, Cohn, Anthony, Pierrehumbert, Janet B.
Planning is a fundamental property of human intelligence. Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs. Can large language models (LLMs) succeed at this task? Here, we pre
Externí odkaz:
http://arxiv.org/abs/2402.02805
Autor:
Soldaini, Luca, Kinney, Rodney, Bhagia, Akshita, Schwenk, Dustin, Atkinson, David, Authur, Russell, Bogin, Ben, Chandu, Khyathi, Dumas, Jennifer, Elazar, Yanai, Hofmann, Valentin, Jha, Ananya Harsh, Kumar, Sachin, Lucy, Li, Lyu, Xinxi, Lambert, Nathan, Magnusson, Ian, Morrison, Jacob, Muennighoff, Niklas, Naik, Aakanksha, Nam, Crystal, Peters, Matthew E., Ravichander, Abhilasha, Richardson, Kyle, Shen, Zejiang, Strubell, Emma, Subramani, Nishant, Tafjord, Oyvind, Walsh, Pete, Zettlemoyer, Luke, Smith, Noah A., Hajishirzi, Hannaneh, Beltagy, Iz, Groeneveld, Dirk, Dodge, Jesse, Lo, Kyle
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to
Externí odkaz:
http://arxiv.org/abs/2402.00159
Autor:
Magnusson, Ian, Bhagia, Akshita, Hofmann, Valentin, Soldaini, Luca, Jha, Ananya Harsh, Tafjord, Oyvind, Schwenk, Dustin, Walsh, Evan Pete, Elazar, Yanai, Lo, Kyle, Groeneveld, Dirk, Beltagy, Iz, Hajishirzi, Hannaneh, Smith, Noah A., Richardson, Kyle, Dodge, Jesse
Language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains$\unicode{x2013}$varying distributions of language. Rather than assuming perplexity on one distribut
Externí odkaz:
http://arxiv.org/abs/2312.10523
Autor:
Weissweiler, Leonie, Hofmann, Valentin, Kantharuban, Anjali, Cai, Anna, Dutt, Ritam, Hengle, Amey, Kabra, Anubha, Kulkarni, Atharva, Vijayakumar, Abhishek, Yu, Haofei, Schütze, Hinrich, Oflazer, Kemal, Mortensen, David R.
Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relatively few systematic inquiries into the linguistic capabilities of the la
Externí odkaz:
http://arxiv.org/abs/2310.15113
We propose a fully unsupervised method to detect bias in contextualized embeddings. The method leverages the assortative information latently encoded by social networks and combines orthogonality regularization, structured sparsity learning, and grap
Externí odkaz:
http://arxiv.org/abs/2212.07547
Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central building blocks of language, i.e., li
Externí odkaz:
http://arxiv.org/abs/2210.13181
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We propose a first model for CaMEL that uses a massively multilingual corpus
Externí odkaz:
http://arxiv.org/abs/2203.10010