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The KV-Cache technique has become the standard for the inference of large language models (LLMs).
The second pascal recognising textual entailment challenge
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., and Szpektor, I · 2006
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Roemmele, M., Bejan, C. A., and Gordon, A. S · 2011
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Decoupled weight decay regularization, 2017
Loshchilov, I. and Hutter, F · 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
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Understanding straight-through estimator in training activation quantized neural nets
Yin, P., Lyu, J., Zhang, S., Osher, S., Qi, Y., and Xin, J · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y · 2020
Cited alongside, same era.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Cited alongside, same era.
Spatten: Efficient sparse attention architecture with cascade token and head pruning
Wang, H., Zhang, Z., and Han, S · 2021
Cited alongside, same era.
Longbench: A bilingual, multitask benchmark for long context understanding, 2023
Bai, Y., Lv, X., Zhang, J., Lyu, H., Tang, J., Huang, Z., Du, Z., Liu, X., Zeng, A., Hou, L., Dong, Y., Tang, J., and Li, J · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
Later among the works it cites.
Cold compress: A toolkit for benchmarking kv cache compression approaches, 8 2024
Adams, G., Ladhak, F., Schoelkopf, H., and Biswas, R · 2024
Closest in time.
Dynamic context pruning for efficient and interpretable autoregressive transformers
Anagnostidis, S., Pavllo, D., Biggio, L., Noci, L., Lucchi, A., and Hofmann, T · 2024
Closest in time.
Nacl: A general and effective kv cache eviction framework for llms at inference time
Chen, Y., Wang, G., Shang, J., Cui, S., Zhang, Z., Liu, T., Wang, S., Sun, Y., Yu, D., and Wu, H · 2024
Closest in time.
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
Cited alongside, same era.
Redpajama: An open source recipe to reproduce llama training dataset, April 2023
Computer, T · 2023
Cited alongside, same era.
Opencompass: A universal evaluation platform for foundation models
Contributors, O · 2023
Cited alongside, same era.
Model tells you what to discard: Adaptive kv cache compression for llms
Ge, S., Zhang, Y., Liu, L., Zhang, M., Han, J., and Gao, J · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Closest in time.
A2sf: Accumulative attention scoring with forgetting factor for token pruning in transformer decoder
Jo, H. R. and Shin, D. K · 2024
Closest in time.
Transformers are multi-state rnns
Oren, M., Hassid, M., Adi, Y., and Schwartz, R · 2024
Closest in time.
Gemma 2: Improving open language models at a practical size
Team, G., Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al · 2024
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Efficient streaming language models with attention sinks, 2024
Xiao, G., Tian, Y., Chen, B., Han, S., and Lewis, M · 2024
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H2o: Heavy-hitter oracle for efficient generative inference of large language models
Zhang, Z., Sheng, Y., Zhou, T., Chen, T., Zheng, L., Cai, R., Song, Z., Tian, Y., Ré, C., Barrett, C., et al · 2024
Closest in time.