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Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields.
The nature and development of scientific reasoning: A synthetic view
Lawson, A. E · 2004
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Learning and scientific reasoning
Bao, L., Cai, T., Koenig, K., Fang, K., Han, J., Wang, J., Liu, Q., Ding, L., Cui, L., Luo, Y., et al · 2009
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Aligning statistical and scientific reasoning
Goodman, S. N · 2016
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Generative language modeling for automated theorem proving
Polu, S. and Sutskever, I · 2020
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Molgpt: Molecular generation using a transformer-decoder model
Bagal, V., Aggarwal, R., Vinod, P. K., and Priyakumar, U. D · 2021
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Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
Chefer, H., Gur, S., and Wolf, L · 2021
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
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Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
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Addressing bias in big data and ai for health care: A call for open science
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., and Tzovara, A · 2021
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Improving multimodal interactive agents with reinforcement learning from human feedback
Abramson, J., Ahuja, A., Carnevale, F., Georgiev, P., Goldin, A., Hung, A., Landon, J., Lhotka, J., Lillicrap, T., Muldal, A., et al · 2022
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A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
Bayoudh, K., Knani, R., Hamdaoui, F., and Mtibaa, A · 2022
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Bidirectional generation of structure and properties through a single molecular foundation model
Chang, J. and Ye, J.-C · 2022
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Towards artificial general intelligence via a multimodal foundation model
Fei, N., Lu, Z., Gao, Y., Yang, G., Huo, Y., Wen, J., Lu, H., Song, R., Gao, X., Xiang, T., et al · 2022
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Towards reasoning in large language models: A survey
Huang, J. and Chang, K. C.-C · 2022
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Hypertree proof search for neural theorem proving
Lample, G., Lachaux, M.-A., Lavril, T., Martinet, X., Hayat, A., Ebner, G., Rodriguez, A., and Lacroix, T · 2022
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Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V. V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., Wu, Y., Neyshabur, B., Gur-Ari, G., and Misra, V · 2022
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Biogpt: Generative pre-trained transformer for biomedical text generation and mining
Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., and Liu, T.-Y · 2022
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Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Rahate, A., Walambe, R., Ramanna, S., and Kotecha, K · 2022
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Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R · 2022
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Multimodal research in vision and language: A review of current and emerging trends
Uppal, S., Bhagat, S., Hazarika, D., Majumder, N., Poria, S., Zimmermann, R., and Zadeh, A · 2022
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Jiuzhang: A chinese pre-trained language model for mathematical problem understanding
Zhao, W. X., Zhou, K., Gong, Z., Zhang, B., Zhou, Y., Sha, J., Chen, Z., Wang, S., Liu, C., and rong Wen, J · 2022
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Have llms advanced enough? a challenging problem solving benchmark for large language models
Arora, D., Singh, H. G., et al · 2023
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Llemma: An open language model for mathematics
Azerbayev, Z., Schoelkopf, H., Paster, K., Santos, M. D., McAleer, S. M., Jiang, A. Q., Deng, J., Biderman, S., and Welleck, S · 2023
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Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond
Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J · 2023
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Science in the age of large language models
Birhane, A., Kasirzadeh, A., Leslie, D., and Wachter, S · 2023
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Theoremqa: A theorem-driven question answering dataset
Chen, W., Yin, M., Ku, M., Lu, P., Wan, Y., Ma, X., Xu, J., Wang, X., and Xia, T · 2023
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Fine-tuning large language models in education
Chen, Y., Chen, H., and Su, S · 2023
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Generative ai for math: Abel
Chern, E., Zou, H., Li, X., Hu, J., Feng, K., Li, J., and Liu, P · 2023
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Fu, J.-Y., Lin, L., Gao, X., Liu, P., Chen, Z., Yang, Z., Zhang, S., Zheng, X., Li, Y., Liu, Y., Ye, X., Liao, Y., Liao, C., Chen, B., Song, C., Wan, J., Lin, Z., Zhang, F., Wang, Z., Zhang, D., and Gai, K · 2023
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What can large language models do in chemistry? a comprehensive benchmark on eight tasks
Guo, T., Nan, B., Liang, Z., Guo, Z., Chawla, N., Wiest, O., Zhang, X., et al · 2023
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Reasoning with language model is planning with world model
Hao, S., Gu, Y., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z · 2023
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Scitune: Aligning large language models with scientific multimodal instructions
Horawalavithana, S., Munikoti, S., Stewart, I., and Kvinge, H · 2023
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al · 2023
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Paperqa: Retrieval-augmented generative agent for scientific research
Lála, J., O’Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G., and White, A. D · 2023
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nach0: multimodal natural and chemical languages foundation model
Livne, M., Miftahutdinov, Z., Tutubalina, E., Kuznetsov, M., Polykovskiy, D., Brundyn, A., Jhunjhunwala, A., Costa, A. B., Aliper, A., and Zhavoronkov, A · 2023
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Mathvista: Evaluating math reasoning in visual contexts with gpt-4v, bard, and other large multimodal models
Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., and Gao, J · 2023
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Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Luo, H., Sun, Q., Xu, C., Zhao, P., Lou, J.-G., Tao, C., Geng, X., Lin, Q., Chen, S., and Zhang, D · 2023
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Bioinspiredllm: Conversational large language model for the mechanics of biological and bio‐inspired materials
Luu, R. K. and Buehler, M · 2023
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Recent advances in natural language processing via large pre-trained language models: A survey
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., and Roth, D · 2023
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Scientists’ perspectives on the potential for generative ai in their fields
Morris, M. R · 2023
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Scaling data-constrained language models
Muennighoff, N., Rush, A., Barak, B., Le Scao, T., Tazi, N., Piktus, A., Pyysalo, S., Wolf, T., and Raffel, C. A · 2023
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Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications
Mulla, N. and Gharpure, P · 2023
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Climax: A foundation model for weather and climate
Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K., and Grover, A · 2023
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Pan, L., Saxon, M., Xu, W., Nathani, D., Wang, X., and Wang, W. Y · 2023
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Large language models are zero shot hypothesis proposers
Qi, B., Zhang, K., Li, H., Tian, K., Zeng, S., Chen, Z.-R., and Zhou, B · 2023
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Large language models can be easily distracted by irrelevant context
Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E. H., Schärli, N., and Zhou, D · 2023
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Sleeveio: modular and reconfigurable platform for multimodal wearable haptic feedback interactions
Shtarbanov, A., Zhu, M., Colonnese, N., and Hajiagha Memar, A · 2023
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A survey of reasoning with foundation models
Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Qiu, J., Xu, J., Ding, M., Li, H., Geng, M., et al · 2023
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Mathgpt official platform, 2023
TALEducation · 2023
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Explainable multi-task learning for multi-modality biological data analysis
Tang, X., Zhang, J., He, Y., Zhang, X., Lin, Z., Partarrieu, S., Hanna, E. B., Ren, Z., Shen, H., Yang, Y., et al · 2023
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Emergent analogical reasoning in large language models
Webb, T., Holyoak, K. J., and Lu, H · 2023
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The future of chemistry is language
White, A. D · 2023
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The role of creative thinking in children’s scientific reasoning
Willemsen, R. H., de Vink, I. C., Kroesbergen, E. H., and Lazonder, A. W · 2023
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The rise and potential of large language model based agents: A survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al · 2023
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Mole-bert: Rethinking pre-training graph neural networks for molecules
Xia, J., Zhao, C., Hu, B., Gao, Z., Tan, C., Liu, Y., Li, S., and Li, S. Z · 2023
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Darwin series: Domain specific large language models for natural science
Xie, T., Wan, Y., Huang, W., Yin, Z., Liu, Y., Wang, S., Linghu, Q., Kit, C., Grazian, C., Zhang, W., Razzak, I., and Hoex, B · 2023
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Metamath: Bootstrap your own mathematical questions for large language models
Yu, L. L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J. T., Li, Z., Weller, A., and Liu, W · 2023
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Mammoth: Building math generalist models through hybrid instruction tuning
Yue, X., Qu, X., Zhang, G., Fu, Y., Huang, W., Sun, H., Su, Y., and Chen, W · 2023
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M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models
Zhang, W., Aljunied, M., Gao, C., Chia, Y. K., and Bing, L · 2023
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k0-math official platform, 2024
MoonshotAI · 2024
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Foundation models in shaping the future of ecology
Morera, A · 2024
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Beyond lines and circles: Unveiling the geometric reasoning gap in large language models
Mouselinos, S., Michalewski, H., and Malinowski, M · 2024
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Xwin-lm: Strong and scalable alignment practice for llms
Ni, B., Hu, J., Wei, Y., Peng, H., Zhang, Z., Meng, G., and Hu, H · 2024
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Mitigating hallucinations in large language models via self-refinement-enhanced knowledge retrieval
Niu, M., Li, H., Shi, J., Haddadi, H., and Mo, F · 2024
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2024
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From methods to datasets: A survey on image-caption generators
Agarwal, L. and Verma, B · 2024
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Probing the limitations of multimodal language models for chemistry and materials research
Alampara, N., Schilling-Wilhelmi, M., Ríos-García, M., Mandal, I., Khetarpal, P., Grover, H. S., Krishnan, N., and Jablonka, K. M · 2024
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A survey on data selection for language models
Albalak, A., Elazar, Y., Xie, S. M., Longpre, S., Lambert, N., Wang, X., Muennighoff, N., Hou, B., Pan, L., Jeong, H., et al · 2024
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Multimodal large language models in health care: applications, challenges, and future outlook
AlSaad, R., Abd-Alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., and Sheikh, J · 2024
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Claude 3.5 sonnet model card addendum
Anthropic, A · 2024
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Numinamath 7b tir
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Xai meets llms: A survey of the relation between explainable ai and large language models
Cambria, E., Malandri, L., Mercorio, F., Nobani, N., and Seveso, A · 2024
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Skywork-o1 open series
o1 Team, S · 2024
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Potential to use metaverse for future teaching and learning
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The language of creativity: Evidence from humans and large language models
Orwig, W., Edenbaum, E. R., Greene, J. D., and Schacter, D. L · 2024
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Unifying large language models and knowledge graphs: A roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X · 2024
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Survey of large multimodal model datasets, application categories and taxonomy
Pattnayak, P., Patel, H. L., Kumar, B., Agarwal, A., Banerjee, I., Panda, S., and Kumar, T · 2024
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Pelletier, A. R., Ramirez, J., Adam, I., Sankar, S., Yan, Y., Wang, D., Steinecke, D., Wang, W., and Ping, P · 2024
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Opportunities for retrieval and tool augmented large language models in scientific facilities
Prince, M. H., Chan, H., Vriza, A., Zhou, T., Sastry, V. K., Luo, Y., Dearing, M. T., Harder, R. J., Vasudevan, R. K., and Cherukara, M. J · 2024
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Large language models as biomedical hypothesis generators: a comprehensive evaluation
Qi, B., Zhang, K., Tian, K., Li, H., Chen, Z.-R., Zeng, S., Hua, E., Jinfang, H., and Zhou, B · 2024
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We-math: Does your large multimodal model achieve human-like mathematical reasoning?
Qiao, R., Tan, Q., Dong, G., Wu, M., Sun, C., Song, X., GongQue, Z., Lei, S., Wei, Z., Zhang, M., et al · 2024
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Qwq: Reflect deeply on the boundaries of the unknown, november 2024
Qwen · 2024
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A survey on security and privacy of large multimodal deep learning models: Teaching and learning perspective
Rahman, M. A., Alqahtani, L., Albooq, A., and Ainousah, A · 2024
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Towards scientific discovery with generative ai: Progress, opportunities, and challenges
Reddy, C. K. and Shojaee, P · 2024
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Pixellm: Pixel reasoning with large multimodal model
Ren, Z., Huang, Z., Wei, Y., Zhao, Y., Fu, D., Feng, J., and Jin, X · 2024
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Charting new territories: Exploring the geographic and geospatial capabilities of multimodal llms
Roberts, J., Lüddecke, T., Sheikh, R., Han, K., and Albanie, S · 2024
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Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions
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Sato, K · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J.-M., Zhang, M., Li, Y. K., Wu, Y., and Guo, D · 2024
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Llm-sr: Scientific equation discovery via programming with large language models
Shojaee, P., Meidani, K., Gupta, S., Farimani, A. B., and Reddy, C. K · 2024
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Language agents achieve superhuman synthesis of scientific knowledge
Skarlinski, M. D., Cox, S., Laurent, J. M., Braza, J. D., Hinks, M., Hammerling, M. J., Ponnapati, M., Rodriques, S. G., and White, A. D · 2024
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A large encoder-decoder family of foundation models for chemical language
Soares, E., Shirasuna, V., Brazil, E. V., Cerqueira, R., Zubarev, D., and Schmidt, K · 2024
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Large language models are inconsistent and biased evaluators
Stureborg, R., Alikaniotis, D., and Suhara, Y · 2024
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Sun, K., Bai, Y., Qi, J., Hou, L., and Li, J · 2024
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Evaluation of search-enabled pre-trained large language models on retrieval tasks for the pubchem database
Sze, A. and Hassoun, S · 2024
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Video captioning–a survey
Vaishnavi, J. and Narmatha, V · 2024
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Lighthouse: A survey of agi hallucination
Wang, F · 2024
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Large language models are not fair evaluators
Wang, P., Li, L., Chen, L., Cai, Z., Zhu, D., Lin, B., Cao, Y., Kong, L., Liu, Q., Liu, T., and Sui, Z · 2024
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Wei, J., Tan, C., Gao, Z., Sun, L., Li, S., Yu, B., Guo, R., and Li, S. Z · 2024
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Georeasoner: Reasoning on geospatially grounded context for natural language understanding
Yan, Y. and Lee, J · 2024
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Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web
Yan, Y., Wen, H., Zhong, S., Chen, W., Chen, H., Wen, Q., Zimmermann, R., and Liang, Y · 2024
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AGIEval: A human-centric benchmark for evaluating foundation models
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