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Recent advances have been improving the context windows of Large Language Models (LLMs).
A framework for source code search using program patterns
Paul, S. and Prakash, A · 1994
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Crafting papers on machine learning
Langley, P · 2000
Earlier work this paper cites.
A systematic comparison of smoothing techniques for sentence-level BLEU
Chen, B. and Cherry, C · 2014
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search, 2020
Husain, H., Wu, H.-H., Gazit, T., Allamanis, M., and Brockschmidt, M · 2020
Earlier work this paper cites.
L-eval: Instituting standardized evaluation for long context language models, 2023
An, C., Gong, S., Zhong, M., Zhao, X., Li, M., Zhang, J., Kong, L., and Qiu, X · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Ntk-aware scaled rope allows llama models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation
bloc97 · 2023
Earlier work this paper cites.
Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion
Ding, Y., Wang, Z., Ahmad, W. U., Ding, H., Tan, M., Jain, N., Ramanathan, M. K., Nallapati, R., Bhatia, P., Roth, D., and Xiang, B · 2023
Earlier work this paper cites.
Llmtest needle in a haystack – pressure testing llms
gkamradt · 2023
Cited alongside, same era.
Swe-bench: Can language models resolve real-world github issues?, 2023
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K · 2023
Cited alongside, same era.
Repobench: Benchmarking repository-level code auto-completion systems
Liu, T., Xu, C., and McAuley, J · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Code llama: Open foundation models for code, 2023
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., Kozhevnikov, A., Evtimov, I., Bitton, J., Bhatt, M., Ferrer, C. C., Grattafiori, A., Xiong, W., Défossez, A., Copet, J., Azhar, F., Touvron, H., Martin, L., Usunier, N., Scialom, T., and Synnaeve, G · 2023
Cited alongside, same era.
Can’t remember details in long documents? you need some r&r
Agrawal, D., Gao, S., and Gajek, M · 2024
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Introducing the next generation of claude anthropic
Anthropic · 2024
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Github code search
GitHub · 2024
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Ruler: What’s the real context size of your long-context language models?, 2024
Hsieh, C.-P., Sun, S., Kriman, S., Acharya, S., Rekesh, D., Jia, F., Zhang, Y., and Ginsburg, B · 2024
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Mixtral of experts, 2024
Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de las Casas, D., Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T. L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2024
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Shaham, U., Ivgi, M., Efrat, A., Berant, J., and Levy, O · 2023
Cited alongside, same era.
Phi-3 technical report: A highly capable language model locally on your phone, 2024
Abdin, M., Jacobs, S. A., Awan, A. A., Aneja, J., Awadallah, A., Awadalla, H., Bach, N., Bahree, A., Bakhtiari, A., Bao, J., Behl, H., Benhaim, A., Bilenko, M., Bjorck, J., Bubeck, S., Cai, Q., Cai, M., Mendes, C. C. T., Chen, W., Chaudhary, V., Chen, D., Chen, D., Chen, Y.-C., Chen, Y.-L., Chopra, P., Dai, X., Giorno, A. D., de Rosa, G., Dixon, M., Eldan, R., Fragoso, V., Iter, D., Gao, M., Gao, M., Gao, J., Garg, A., Goswami, A., Gunasekar, S., Haider, E., Hao, J., Hewett, R. J., Huynh, J., Javaheripi, M., Jin, X., Kauffmann, P., Karampatziakis, N., Kim, D., Khademi, M., Kurilenko, L., Lee, J. R., Lee, Y. T., Li, Y., Li, Y., Liang, C., Liden, L., Liu, C., Liu, M., Liu, W., Lin, E., Lin, Z., Luo, C., Madan, P., Mazzola, M., Mitra, A., Modi, H., Nguyen, A., Norick, B., Patra, B., Perez-Becker, D., Portet, T., Pryzant, R., Qin, H., Radmilac, M., Rosset, C., Roy, S., Ruwase, O., Saarikivi, O., Saied, A., Salim, A., Santacroce, M., Shah, S., Shang, N., Sharma, H., Shukla, S., Song, X., Tanaka, M., Tupini, A., Wang, X., Wang, L., Wang, C., Wang, Y., Ward, R., Wang, G., Witte, P., Wu, H., Wyatt, M., Xiao, B., Xu, C., Xu, J., Xu, W., Yadav, S., Yang, F., Yang, J., Yang, Z., Yang, Y., Yu, D., Yuan, L., Zhang, C., Zhang, C., Zhang, J., Zhang, L. L., Zhang, Y., Zhang, Y., Zhang, Y., and Zhou, X · 2024
Cited alongside, same era.
Gemini: A family of highly capable multimodal models, 2024a
Gemini Team
Cited in the paper.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024b
Gemini Team
Cited in the paper.
Lozhkov, A., Li, R., Allal, L. B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., et al · 2024
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∞ \infty bench: Extending long context evaluation beyond 100k tokens, 2024
Zhang, X., Chen, Y., Hu, S., Xu, Z., Chen, J., Hao, M. K., Han, X., Thai, Z. L., Wang, S., Liu, Z., and Sun, M · 2024
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