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Large Language Models (LLMs) excel at a wide range of tasks, but adapting them to new data, particularly for personalized applications, poses significant challenges due to resource and computational constraints.
ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
Earlier work this paper cites.
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
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Toy models of superposition
Elhage, N., Hume, T., Olsson, C., Schiefer, N., Henighan, T., Kravec, S., Hatfield-Dodds, Z., Lasenby, R., Drain, D., Chen, C., Grosse, R., McCandlish, S., Kaplan, J., Amodei, D., Wattenberg, M., and Olah, C · 2022
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LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Earlier work this paper cites.
Branch-train-merge: Embarrassingly parallel training of expert language models, 2022
Li, M., Gururangan, S., Dettmers, T., Lewis, M., Althoff, T., Smith, N. A., and Zettlemoyer, L · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., and Schmidt, L · 2022
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Towards monosemanticity: Decomposing language models with dictionary learning
Bricken, T., Templeton, A., Batson, J., Chen, B., Jermyn, A., Conerly, T., Turner, N., Anil, C., Denison, C., Askell, A., Lasenby, R., Wu, Y., Kravec, S., Schiefer, N., Maxwell, T., Joseph, N., Hatfield-Dodds, Z., Tamkin, A., Nguyen, K., McLean, B., Burke, J. E., Hume, T., Carter, S., Henighan, T., and Olah, C · 2023
Earlier work this paper cites.
Adapting language models to compress contexts
Chevalier, A., Wettig, A., Ajith, A., and Chen, D · 2023
Earlier work this paper cites.
Task arithmetic with lora for continual learning, 2023
Chitale, R., Vaidya, A., Kane, A., and Ghotkar, A · 2023
Earlier work this paper cites.
On the computational complexity of self-attention
Duman Keles, F., Wijewardena, P. M., and Hegde, C · 2023
Cited alongside, same era.
LLMLingua: Compressing prompts for accelerated inference of large language models
Jiang, H., Wu, Q., Lin, C.-Y., Yang, Y., and Qiu, L · 2023
Cited alongside, same era.
Learning to compress prompts with gist tokens
Mu, J., Li, X., and Goodman, N · 2023
Cited alongside, same era.
Efficient prompting via dynamic in-context learning, 2023
Zhou, W., Jiang, Y. E., Cotterell, R., and Sachan, M · 2023
Cited alongside, same era.
PerLTQA: A personal long-term memory dataset for memory classification, retrieval, and fusion in question answering
Du, Y., Wang, H., Zhao, Z., Liang, B., Wang, B., Zhong, W., Wang, Z., and Wong, K.-F · 2024
Cited alongside, same era.
Towards modular LLMs by building and reusing a library of LoRAs
Ostapenko, O., Su, Z., Ponti, E., Charlin, L., Le Roux, N., Caccia, L., and Sordoni, A · 2024
Later among the works it cites.
The linear representation hypothesis and the geometry of large language models
Park, K., Choe, Y. J., and Veitch, V · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale, 2024
Penedo, G., Kydlíček, H., allal, L. B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L. V., and Wolf, T · 2024
Later among the works it cites.
Salemi, A. and Zamani, H · 2024
Later among the works it cites.
LaMP: When large language models meet personalization
Salemi, A., Mysore, S., Bendersky, M., and Zamani, H · 2024
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Ge, T., Jing, H., Wang, L., Wang, X., Chen, S.-Q., and Wei, F · 2024
Cited alongside, same era.
Arcee’s MergeKit: A toolkit for merging large language models
Goddard, C., Siriwardhana, S., Ehghaghi, M., Meyers, L., Karpukhin, V., Benedict, B., McQuade, M., and Solawetz, J · 2024
Cited alongside, same era.
LongLLMLingua: Accelerating and enhancing LLMs in long context scenarios via prompt compression
Jiang, H., Wu, Q., , Luo, X., Li, D., Lin, C.-Y., Yang, Y., and Qiu, L · 2024
Cited alongside, same era.
Inducing generalization across languages and tasks using featurized low-rank mixtures, 2024
Lin, C.-C., Wang, X., Clark, J. H., Lu, H., Zhu, Y., Whitehouse, C., and Yu, H · 2024
Cited alongside, same era.
Lost in the middle: How language models use long contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P · 2024
Cited alongside, same era.
Llms + persona-plug = personalized llms, 2024a
Liu, J., Zhu, Y., Wang, S., Wei, X., Min, E., Lu, Y., Wang, S., Yin, D., and Dou, Z
Cited in the paper.
Optimization methods for personalizing large language models through retrieval augmentation
Salemi, A., Kallumadi, S., and Zamani, H
Cited in the paper.
Parameter-efficient fine-tuning in large models: A survey of methodologies, 2024
Wang, L., Chen, S., Jiang, L., Pan, S., Cai, R., Yang, S., and Yang, F · 2024
Later among the works it cites.
Mixture of loRA experts
Wu, X., Huang, S., and Wei, F · 2024
Later among the works it cites.
Language models are super mario: Absorbing abilities from homologous models as a free lunch
Yu, L., Yu, B., Yu, H., Huang, F., and Li, Y · 2024
Later among the works it cites.
Hydra: Model factorization framework for black-box llm personalization, 2024
Zhuang, Y., Sun, H., Yu, Y., Qiang, R., Wang, Q., Zhang, C., and Dai, B · 2024
Later among the works it cites.
Ziplora: Any subject in any style by effectively merging loras
Shah, V., Ruiz, N., Cole, F., Lu, E., Lazebnik, S., Li, Y., and Jampani, V · 2025
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