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The question-answering (QA) capabilities of foundation models are highly sensitive to prompt variations, rendering their performance susceptible to superficial, non-meaning-altering changes.
Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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Undoing the damage of dataset bias
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., and Torralba, A · 2012
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Microsoft COCO: common objects in context
Lin, T., Maire, M., Belongie, S. J., Bourdev, L. D., Girshick, R. B., Hays, J., Perona, P., Ramanan, D., Doll’a r, P., and Zitnick, C. L · 2014
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Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., and Narayanan, A · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., and Rush, A · 2020
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A mathematical framework for transformer circuits
Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., DasSarma, N., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., Amodei, D., Brown, T., Clark, J., Kaplan, J., McCandlish, S., and Olah, C · 2021
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Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S · 2021
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Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
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Discovering latent knowledge in language models without supervision
Burns, C., Ye, H., Klein, D., and Steinhardt, J · 2022
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Emergent world representations: Exploring a sequence model trained on a synthetic task
Li, K., Hopkins, A. K., Bau, D., Viégas, F., Pfister, H., and Wattenberg, M · 2022
Cited alongside, same era.
Advances, challenges and opportunities in creating data for trustworthy ai
Liang, W., Tadesse, G. A., Ho, D., Fei-Fei, L., Zaharia, M., Zhang, C., and Zou, J · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu, S.-C., Tafjord, O., Clark, P., and Kalyan, A · 2022
Cited alongside, same era.
Relative representations enable zero-shot latent space communication
Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., and Rodola, E · 2022
Cited alongside, same era.
Extracting latent steering vectors from pretrained language models
Mme: A comprehensive evaluation benchmark for multimodal large language models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al · 2023
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Finding neurons in a haystack: Case studies with sparse probing, may 2023
Gurnee, W., Nanda, N., Pauly, M., et al · 2023
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Obelisc: An open web-scale filtered dataset of interleaved image-text documents
Laurençon, H., Saulnier, L., Tronchon, L., Bekman, S., Singh, A., Lozhkov, A., Wang, T., Karamcheti, S., Rush, A. M., Kiela, D., et al · 2023
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Inference-time intervention: Eliciting truthful answers from a language model, 2023
Li, K., Patel, O., Viégas, F., Pfister, H., and Wattenberg, M · 2023
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Large language models sensitivity to the order of options in multiple-choice questions
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Subramani, N., Suresh, N., and Peters, M. E · 2022
Cited alongside, same era.
Contrastive adapters for foundation model group robustness
Zhang, M. and Ré, C · 2022
Cited alongside, same era.
Zero-shot robustification of zero-shot models with foundation models
Adila, D., Shin, C., Cai, L., and Sala, F · 2023
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2023
Cited alongside, same era.
Instructblip: Towards general-purpose vision-language models with instruction tuning. arxiv 2023
Dai, W., Li, J., Li, D., Tiong, A., Zhao, J., Wang, W., Li, B., Fung, P., and Hoi, S · 2023
Cited alongside, same era.
Improved baselines with visual instruction tuning, 2023a
Liu, H., Li, C., Li, Y., and Lee, Y. J
Cited in the paper.
Visual instruction tuning, 2023b
Liu, H., Li, C., Wu, Q., and Lee, Y. J
Cited in the paper.
Liu, S., Xing, L., and Zou, J
Cited in the paper.
Pezeshkpour, P. and Hruschka, E · 2023
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Refocusing is key to transfer learning
Shi, B., Gai, S., Darrell, T., and Wang, X · 2023
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Large language models are not fair evaluators
Wang, P., Li, L., Chen, L., Zhu, D., Lin, B., Cao, Y., Liu, Q., Liu, T., and Sui, Z · 2023
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Mitigating spurious correlations in multi-modal models during fine-tuning
Yang, Y., Nushi, B., Palangi, H., and Mirzasoleiman, B · 2023
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Tell your model where to attend: Post-hoc attention steering for llms
Zhang, Q., Singh, C., Liu, L., Liu, X., Yu, B., Gao, J., and Zhao, T · 2023
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