Zobrazeno 1 - 10
of 671
pro vyhledávání: '"Hinck, A. P."'
Although capable of generating creative text, Large Language Models (LLMs) are poor judges of what constitutes "creativity". In this work, we show that we can leverage this knowledge of how to write creatively in order to better judge what is creativ
Externí odkaz:
http://arxiv.org/abs/2412.06060
Autor:
Ratzlaff, Neale, Olson, Matthew Lyle, Hinck, Musashi, Aflalo, Estelle, Tseng, Shao-Yen, Lal, Vasudev, Howard, Phillip
Large Multi-Modal Models (LMMs) have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input, such as an image. However, their responses are influenced by societal biases present in the
Externí odkaz:
http://arxiv.org/abs/2411.12590
Autor:
Ratzlaff, Neale, Olson, Matthew Lyle, Hinck, Musashi, Tseng, Shao-Yen, Lal, Vasudev, Howard, Phillip
Large Vision Language Models (LVLMs) such as LLaVA have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input image. However, their responses are influenced by societal biases present
Externí odkaz:
http://arxiv.org/abs/2410.13976
We introduce AutoPersuade, a three-part framework for constructing persuasive messages. First, we curate a large dataset of arguments with human evaluations. Next, we develop a novel topic model to identify argument features that influence persuasive
Externí odkaz:
http://arxiv.org/abs/2410.08917
Autor:
Yu, Sungduk, White, Brian L., Bhiwandiwalla, Anahita, Hinck, Musashi, Olson, Matthew Lyle, Nguyen, Tung, Lal, Vasudev
Detecting and attributing temperature increases due to climate change is crucial for understanding global warming and guiding adaptation strategies. The complexity of distinguishing human-induced climate signals from natural variability has challenge
Externí odkaz:
http://arxiv.org/abs/2408.15993
Autor:
Hinck, Musashi, Holtermann, Carolin, Olson, Matthew Lyle, Schneider, Florian, Yu, Sungduk, Bhiwandiwalla, Anahita, Lauscher, Anne, Tseng, Shaoyen, Lal, Vasudev
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significantly increases the likelihood of the model returning an English response
Externí odkaz:
http://arxiv.org/abs/2407.02333
We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs). Of particular interest is the 2B parameter Gemma model, which provides opportunities to
Externí odkaz:
http://arxiv.org/abs/2404.01331
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
In computational social science (CSS), researchers analyze documents to explain social and political phenomena. In most scenarios, CSS researchers first obtain labels for documents and then explain labels using interpretable regression analyses in th
Externí odkaz:
http://arxiv.org/abs/2306.04746
Autor:
Hinck, Daniel C., Schöttler, Jonas J., Krantz, Maria, Isleif, Katharina-Sophie, Niggemann, Oliver
The protection of non-combatants in times of (fully) autonomous warfare raises the question of the timeliness of the international protective emblem. Incidents in the recent past indicate that it is becoming necessary to transfer the protective emble
Externí odkaz:
http://arxiv.org/abs/2305.05459