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As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains.
Pragmatics
Levinson, S. C · 1983
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The limited capacity model of mediated message processing
Lang, A · 2000
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“cool” communication in the classroom: A preliminary examination of student perceptions of instructor use of positive slang
Mazer, J. P. and Hunt, S. K · 2008
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Improving prescription drug warnings to promote patient comprehension
Wolf, M. S., Davis, T. C., Bass, P. F., Curtis, L. M., Lindquist, L. A., Webb, J. A., Bocchini, M. V., Bailey, S. C., and Parker, R. M · 2009
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Chapter 11: Information and persuasion
Brown, M. and Bruhn, C · 2011
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Chapter 7: Quantitative information
Fagerlin, A. and Peters, E · 2011
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The seduction of easiness: How science depictions influence laypeople’s reliance on their own evaluation of scientific information
Scharrer, L., Bromme, R., Britt, M. A., and Stadtler, M · 2011
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Effective communication of uncertainty in the ipcc reports
Budescu, D. V., Por, H.-H., and Broomell, S. B · 2012
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An instrument for assessing scientists’ written skills in public communication of science
Baram-Tsabari, A. and Lewenstein, B. V · 2013
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Multidimensional quality metrics: a flexible system for assessing translation quality
Lommel, A., Burchardt, A., and Uszkoreit, H · 2013
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The dynamics of crowdfunding: An exploratory study
Mollick, E · 2013
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The ethics of scientific communication under uncertainty
Keohane, R. O., Lane, M., and Oppenheimer, M · 2014
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Predictors of public climate change awareness and risk perception around the world
Lee, T. M., Markowitz, E. M., Howe, P. D., Ko, C.-Y., and Leiserowitz, A. A · 2015
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The scientific consensus on climate change as a gateway belief: experimental evidence
van der Linden, S. L., Leiserowitz, A. A., Feinberg, G. D., and Maibach, E. W · 2015
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Concrete problems in AI safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P. F., Schulman, J., and Mané, D · 2016
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Trust in science and the science of trust
Hendriks, F., Kienhues, D., and Bromme, R · 2016
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User comments on climate stories: impacts of anecdotal vs. scientific evidence
Hinnant, A., Subramanian, R., and Young, R · 2016
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Reflections on climate change communication research and practice in the second decade of the 21st century: what more is there to say?
Moser, S · 2016
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The Oxford Handbook of the Science of Science Communication
Jamieson, K. H., Kahan, D. M., and Scheufele, D. A · 2017
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Affective imagery, risk perceptions, and climate change communication
Leiserowitz, A. and Smith, N · 2017
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Supervising strong learners by amplifying weak experts
Christiano, P. F., Shlegeris, B., and Amodei, D · 2018
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Emotionalization in science communication: The impact of narratives and visual representations on knowledge gain and risk perception
Flemming, D., Cress, U., Kimmig, S., Brandt, M., and Kimmerle, J · 2018
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When facts are not enough
Hayhoe, K · 2018
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Irving, G., Christiano, P. F., and Amodei, D · 2018
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Scalable agent alignment via reward modeling: a research direction
Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., and Legg, S · 2018
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The Oxford encyclopedia of climate change communication
Nisbet, M. C., Ho, S. S., Markowitz, E., O’Neill, S., Schäfer, M. S., and Thaker, J. (eds.) · 2018
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The different audiences of science communication: A segmentation analysis of the swiss population’s perceptions of science and their information and media use patterns
Schäfer, M. S., Füchslin, T., Metag, J., Kristiansen, S., and Rauchfleisch, A · 2018
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Elegant science narratives and unintended influences: An agenda for the science of science communication
Blanton, H. and Ikizer, E. G · 2019
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Acknowledging uncertainty impacts public acceptance of climate scientists’ predictions
Howe, L. C., MacInnis, B., Krosnick, J. A., Markowitz, E. M., and Socolow, R · 2019
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Online and (the feeling of being) informed: Online news usage patterns and their relation to subjective and objective political knowledge
Leonhard, L., Karnowski, V., and Kümpel, A. S · 2019
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The social media life of climate change: Platforms, publics and future imaginaries
Pearce, W., Niederer, S., Özkula, S. M., and Sánchez Querubín, N · 2019
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Jargon use in public understanding of science papers over three decades
Baram-Tsabari, A., Wolfson, O., Yosef, R., Chapnik, N., Brill, A., and Segev, E · 2020
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Planning strategic interaction: Attaining goals through communicative action
Berger, C. R · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., 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., 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., and Amodei, D · 2020
Cited alongside, same era.
Climate-fever: A dataset for verification of real-world climate claims
Diggelmann, T., Boyd-Graber, J., Bulian, J., Ciaramita, M., and Leippold, M · 2020
Cited alongside, same era.
Retrieval augmented language model pre-training
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M · 2020
Cited alongside, same era.
Climate assessment moves local
Holmes, K. J., Wender, B. A., Weisenmiller, R., Doughman, P., and Kerxhalli-Kleinfield, M · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., and Kiela, D · 2020
Cited alongside, same era.
Climate change remains top global threat across 19-country survey, 2022
Poushter, J., Fagan, M., and Gubbala, S · 2022
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Measuring attribution in natural language generation models, 2022
Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., and Reitter, D · 2022
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., and Bengio, Y · 2022
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Two contrasting data annotation paradigms for subjective NLP tasks
Rottger, P., Vidgen, B., Hovy, D., and Pierrehumbert, J · 2022
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Self-critiquing models for assisting human evaluators, 2022
Saunders, W., Yeh, C., Wu, J., Bills, S., Ouyang, L., Ward, J., and Leike, J · 2022
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Combatting climate change misinformation: Evidence for longevity of inoculation and consensus messaging effects
Maertens, R., Anseel, F., and van der Linden, S · 2020
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Psychological and experiential factors affecting climate change perception: learnings from a transnational empirical study and implications for framing climate-related flood events
Munoz-Carrier, G., Thomsen, D., and Pickering, G. J · 2020
Cited alongside, same era.
“don’t tell me what to do”: Resistance to climate change messages suggesting behavior changes
Palm, R., Bolsen, T., and Kingsland, J. T · 2020
Cited alongside, same era.
Introduction to visualizing climate change
Schäfer, M. S · 2020
Cited alongside, same era.
ClimaText: A dataset for climate change topic detection
Varini, F. S., Boyd-Graber, J., Ciaramita, M., and Leippold, M · 2020
Cited alongside, same era.
“It’s global warming, stupid”: Aggressive communication styles and political ideology in science blog debates about climate change
Yuan, S. and Lu, H · 2020
Cited alongside, same era.
Effects of consensus messages and political ideology on climate change attitudes: inconsistent findings and the effect of a pretest
Chinn, S. and Hart, P. S · 2021
Cited alongside, same era.
ClimateBERT: a pretrained language model for climate-related text
Webersinke, N., Kraus, M., Bingler, J., and Leippold, M · 2022
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Probing in context: Toward building robust classifiers via probing large language models, 2023
Amini, A. and Ciaramita, M · 2023
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AI and climate information needs in africa
Amini, A., Buck, C., Brown, H., Bulian, J., Huebscher, M. C., Ciaramita, M., Das, S., Gaiarin, B., Gordon, C., Gupta, R., Kutu, K., Lartey, D. L., Leippold, M., Leuenberger, L., and Mensah, M. A · 2023
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Palm 2 technical report, 2023
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., Chu, E., Clark, J. H., Shafey, L. E., Huang, Y., Meier-Hellstern, K., Mishra, G., Moreira, E., Omernick, M., Robinson, K., Ruder, S., Tay, Y., Xiao, K., Xu, Y., Zhang, Y., Abrego, G. H., Ahn, J., Austin, J., Barham, P., Botha, J., Bradbury, J., Brahma, S., Brooks, K., Catasta, M., Cheng, Y., Cherry, C., Choquette-Choo, C. A., Chowdhery, A., Crepy, C., Dave, S., Dehghani, M., Dev, S., Devlin, J., Díaz, M., Du, N., Dyer, E., Feinberg, V., Feng, F., Fienber, V., Freitag, M., Garcia, X., Gehrmann, S., Gonzalez, L., Gur-Ari, G., Hand, S., Hashemi, H., Hou, L., Howland, J., Hu, A., Hui, J., Hurwitz, J., Isard, M., Ittycheriah, A., Jagielski, M., Jia, W., Kenealy, K., Krikun, M., Kudugunta, S., Lan, C., Lee, K., Lee, B., Li, E., Li, M., Li, W., Li, Y., Li, J., Lim, H., Lin, H., Liu, Z., Liu, F., Maggioni, M., Mahendru, A., Maynez, J., Misra, V., Moussalem, M., Nado, Z., Nham, J., Ni, E., Nystrom, A., Parrish, A., Pellat, M., Polacek, M., Polozov, A., Pope, R., Qiao, S., Reif, E., Richter, B., Riley, P., Ros, A. C., Roy, A., Saeta, B., Samuel, R., Shelby, R., Slone, A., Smilkov, D., So, D. R., Sohn, D., Tokumine, S., Valter, D., Vasudevan, V., Vodrahalli, K., Wang, X., Wang, P., Wang, Z., Wang, T., Wieting, J., Wu, Y., Xu, K., Xu, Y., Xue, L., Yin, P., Yu, J., Zhang, Q., Zheng, S., Zheng, C., Zhou, W., Zhou, D., Petrov, S., and Wu, Y · 2023
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Attributed question answering: Evaluation and modeling for attributed large language models, 2023
Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., Soares, L. B., Ciaramita, M., Eisenstein, J., Ganchev, K., Herzig, J., Hui, K., Kwiatkowski, T., Ma, J., Ni, J., Saralegui, L. S., Schuster, T., Cohen, W. W., Collins, M., Das, D., Metzler, D., Petrov, S., and Webster, K · 2023
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Can large language models be an alternative to human evaluations?
Chiang, C.-H. and Lee, H.-y · 2023
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The dangers of trusting stochastic parrots: Faithfulness and trust in open-domain conversational question answering
Chiesurin, S., Dimakopoulos, D., Sobrevilla Cabezudo, M. A., Eshghi, A., Papaioannou, I., Rieser, V., and Konstas, I · 2023
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Exploring ’quality’ in science communication online: Expert thoughts on how to assess and promote science communication quality in digital media contexts
Fähnrich, B., Weitkamp, E., and Kupper, J. F · 2023
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The devil is in the errors: Leveraging large language models for fine-grained machine translation evaluation, 2023
Fernandes, P., Deutsch, D., Finkelstein, M., Riley, P., Martins, A. F. T., Neubig, G., Garg, A., Clark, J. H., Freitag, M., and Firat, O · 2023
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Gemini: A family of highly capable multimodal models, 2023
Gemini Team · 2023
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Chatgpt outperforms crowd workers for text-annotation tasks
Gilardi, F., Alizadeh, M., and Kubli, M · 2023
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Survey of hallucination in natural language generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., and Fung, P · 2023
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Large language models are state-of-the-art evaluators of translation quality, 2023
Kocmi, T. and Federmann, C · 2023
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How to communicate science to the public? recommendations for effective written communication derived from a systematic review, Aug 2023
König, L. M., Altenmüller, M. S., Fick, J., Crusius, J., Genschow, O., and Sauerland, M · 2023
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Climatex: Do llms accurately assess human expert confidence in climate statements?, 2023
Lacombe, R., Wu, K., and Dilworth, E · 2023
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Evaluating verifiability in generative search engines, 2023
Liu, N. F., Zhang, T., and Liang, P · 2023
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Harnessing the power of communication and behavior science to enhance society’s response to climate change
Maibach, E. W., Uppalapati, S. S., Orr, M., and Thaker, J · 2023
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Michael, J., Mahdi, S., Rein, D., Petty, J., Dirani, J., Padmakumar, V., and Bowman, S. R · 2023
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GPT-4 technical report, 2023
OpenAI · 2023
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Learning from and about climate scientists, 2023
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Measuring Attribution in Natural Language Generation Models
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Large language models encode clinical knowledge
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