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Large language models (LLMs) are increasingly deployed and democratized on edge devices.
Uncertainty in deep learning
Gal, Y. et al · 2016
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Solving general arithmetic word problems
Roy, S. and Roth, D · 2016
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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CoQA: A conversational question answering challenge
Reddy, S., Chen, D., and Manning, C. D · 2019
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Social iqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., Le Bras, R., and Choi, Y · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J · 2019
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Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al · 2020
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Unsupervised quality estimation for neural machine translation
Fomicheva, M., Sun, S., Yankovskaya, L., Blain, F., Guzmán, F., Fishel, M., Aletras, N., Chaudhary, V., and Specia, L · 2020
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 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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How can we know when language models know? on the calibration of language models for question answering
Jiang, Z., Araki, J., Ding, H., and Neubig, G · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Lin, S., Hilton, J., and Evans, O · 2021
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Are nlp models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2021
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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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Language models (mostly) know what they know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al · 2022
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Teaching models to express their uncertainty in words
Lin, S., Hilton, J., and Evans, O · 2022
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Reducing conversational agents’ overconfidence through linguistic calibration
Mielke, S. J., Szlam, A., Dinan, E., and Boureau, Y.-L · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
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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 · 2023
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The internal state of an llm knows when it’s lying
Azaria, A. and Mitchell, T · 2023
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Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al · 2023
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Lm-polygraph: Uncertainty estimation for language models
Fadeeva, E., Vashurin, R., Tsvigun, A., Vazhentsev, A., Petrakov, S., Fedyanin, K., Vasilev, D., Goncharova, E., Panchenko, A., Panov, M., et al · 2023
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Sparsegpt: Massive language models can be accurately pruned in one-shot
Frantar, E. and Alistarh, D · 2023
Cited alongside, same era.
Openllama: An open reproduction of llama, May 2023
Geng, X. and Liu, H · 2023
Cited alongside, same era.
Look before you leap: An exploratory study of uncertainty measurement for large language models
Huang, Y., Song, J., Wang, Z., Zhao, S., Chen, H., Juefei-Xu, F., and Ma, L · 2023
Cited alongside, same era.
Phi-2: The surprising power of small language models, December 2023
Javaheripi, M. and Bubeck, S · 2023
Cited alongside, same era.
Mistral 7b
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2023
Cited alongside, same era.
Hybrid llm: Cost-efficient and quality-aware query routing
Ding, D., Mallick, A., Wang, C., Sim, R., Mukherjee, S., Rühle, V., Lakshmanan, L. V., and Awadallah, A. H · 2024
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A survey on in-context learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al · 2024
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Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models
Duan, J., Cheng, H., Wang, S., Zavalny, A., Wang, C., Xu, R., Kailkhura, B., and Xu, K · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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A survey of confidence estimation and calibration in large language models
Geng, J., Cai, F., Wang, Y., Koeppl, H., Nakov, P., and Gurevych, I · 2024
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Kuhn, L., Gal, Y., and Farquhar, S · 2023
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Le Scao, T., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., et al · 2023
Cited alongside, same era.
Generating with confidence: Uncertainty quantification for black-box large language models
Lin, Z., Trivedi, S., and Sun, J · 2023
Cited alongside, same era.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Manakul, P., Liusie, A., and Gales, M · 2023
Cited alongside, same era.
Rwkv: Reinventing rnns for the transformer era
Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., Grella, M., et al · 2023
Cited alongside, same era.
Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., and Manning, C. D · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
Cited alongside, same era.
Granite 3.0 language models, 2024
Granite Team, I · 2024
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Recurrentgemma: Moving past transformers for efficient open language models, 2024
Griffin, R. and Teams, G · 2024
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Olmo: Accelerating the science of language models
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M. E., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Strubell, E., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Zettlemoyer, L., Dodge, J., Lo, K., Soldaini, L., Smith, N. A., and Hajishirzi, H · 2024
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Decomposing uncertainty for large language models through input clarification ensembling
Hou, B., Liu, Y., Qian, K., Andreas, J., Chang, S., and Zhang, Y · 2024
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Routerbench: A benchmark for multi-llm routing system
Hu, Q. J., Bieker, J., Li, X., Jiang, N., Keigwin, B., Ranganath, G., Keutzer, K., and Upadhyay, S. K · 2024
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Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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Inference-time intervention: Eliciting truthful answers from a language model
Li, K., Patel, O., Viégas, F., Pfister, H., and Wattenberg, M · 2024
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Awq: Activation-aware weight quantization for on-device llm compression and acceleration
Lin, J., Tang, J., Tang, H., Yang, S., Chen, W.-M., Wang, W.-C., Xiao, G., Dang, X., Gan, C., and Han, S · 2024
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Small language models: Survey, measurements, and insights
Lu, Z., Li, X., Cai, D., Yi, R., Liu, F., Zhang, X., Lane, N. D., and Xu, M · 2024
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Factual confidence of llms: on reliability and robustness of current estimators
Mahaut, M., Aina, L., Czarnowska, P., Hardalov, M., Müller, T., and Màrquez, L · 2024
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Llama 3.2: Revolutionizing edge ai and vision with open, customizable models
Meta, A · 2024
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Olmoe: Open mixture-of-experts language models, 2024
Muennighoff, N., Soldaini, L., Groeneveld, D., Lo, K., Morrison, J., Min, S., Shi, W., Walsh, P., Tafjord, O., Lambert, N., Gu, Y., Arora, S., Bhagia, A., Schwenk, D., Wadden, D., Wettig, A., Hui, B., Dettmers, T., Kiela, D., Farhadi, A., Smith, N. A., Koh, P. W., Singh, A., and Hajishirzi, H · 2024
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Routellm: Learning to route llms with preference data
Ong, I., Almahairi, A., Wu, V., Chiang, W.-L., Wu, T., Gonzalez, J. E., Kadous, M. W., and Stoica, I · 2024
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Pfeiffer, P., Singer, P., Babakhin, Y., Fodor, G., Dhankhar, N., and Ambati, S. S · 2024
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Polyrouter: A multi-llm querying system
Stripelis, D., Hu, Z., Zhang, J., Xu, Z., Shah, A., Jin, H., Yao, Y., Avestimehr, S., and He, C · 2024
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Mobillama: Towards accurate and lightweight fully transparent gpt, 2024
Thawakar, O., Vayani, A., Khan, S., Cholakkal, H., Anwer, R. M., Felsberg, M., Baldwin, T., Xing, E. P., and Khan, F. S · 2024
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Xiong, M., Hu, Z., Lu, X., LI, Y., Fu, J., He, J., and Hooi, B · 2024
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Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., Huang, F., Dong, G., Wei, H., Lin, H., Tang, J., Wang, J., Yang, J., Tu, J., Zhang, J., Ma, J., Xu, J., Zhou, J., Bai, J., He, J., Lin, J., Dang, K., Lu, K., Chen, K., Yang, K., Li, M., Xue, M., Ni, N., Zhang, P., Wang, P., Peng, R., Men, R., Gao, R., Lin, R., Wang, S., Bai, S., Tan, S., Zhu, T., Li, T., Liu, T., Ge, W., Deng, X., Zhou, X., Ren, X., Zhang, X., Wei, X., Ren, X., Fan, Y., Yao, Y., Zhang, Y., Wan, Y., Chu, Y., Liu, Y., Cui, Z., Zhang, Z., and Fan, Z · 2024
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Can large language models faithfully express their intrinsic uncertainty in words?
Yona, G., Aharoni, R., and Geva, M · 2024
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Tinyllama: An open-source small language model
Zhang, P., Zeng, G., Wang, T., and Lu, W · 2024
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Eagle: Efficient training-free router for multi-llm inference
Zhao, Z., Jin, S., and Mao, Z. M · 2024
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