Fetching the paper…
Reading the bibliography…
The integration of a complex set of Electronic Design Automation (EDA) tools to enhance interoperability is a critical concern for circuit designers.
P. Chen, D. A. Kirkpatrick, and K. Keutzer, “Scripting for eda tools: a case study,” in Proceedings of the IEEE 2001. 2nd International Symposium on Quality Electronic Design . IEEE, 2001, pp. 87–93
2001
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
T. Ajayi and D. Blaauw, “OpenROAD: Toward a self-driving, open-source digital layout implementation tool chain,” in Proceedings of Government Microcircuit Applications and Critical Technology Conference , 2019
2019
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL , 2019
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “XLNet: Generalized autoregressive pretraining for language understanding,” in Proc. NIPS , 2019
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” in Proc. NIPS , 2020
2020
Earlier work this paper cites.
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “LoRA: Low-Rank Adaptation of Large Language Models,” in Proc. ICLR , 2021
2021
Earlier work this paper cites.
A. Aghajanyan, S. Gupta, and L. Zettlemoyer, “Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning,” in Proc. ACL , 2021
2021
Earlier work this paper cites.
S. M. Xie, A. Raghunathan, P. Liang, and T. Ma, “An explanation of in-context learning as implicit bayesian inference,” in Proc. ICLR , 2021
2021
Earlier work this paper cites.
T. Dettmers, M. Lewis, S. Shleifer, and L. Zettlemoyer, “8-bit optimizers via block-wise quantization,” in Proc. ICLR , 2021
2021
Earlier work this paper cites.
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, and W. Fedus, “Emergent Abilities of Large Language Models,” Journal of Machine Learning Research , 2022
2022
Earlier work this paper cites.
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language model with self generated instructions,” arXiv preprint , 2022
2022
Earlier work this paper cites.
J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le, “Finetuned Language Models are Zero-Shot Learners,” in Proc. ICLR , 2022
2022
Earlier work this paper cites.
Y. Tay, M. Dehghani, V. Q. Tran, X. Garcia, J. Wei, X. Wang, H. W. Chung, D. Bahri, T. Schuster, S. Zheng et al. , “UL2: Unifying language learning paradigms,” in Proc. ICLR , 2022
2022
Earlier work this paper cites.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann et al. , “Palm: Scaling language modeling with pathways,” arXiv preprint , 2022
2022
Cited alongside, same era.
S. Min, M. Lewis, L. Zettlemoyer, and H. Hajishirzi, “MetaICL: Learning to learn in context,” in Proc. ACL , 2022
2022
Cited alongside, same era.
Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, and Z. Sui, “A survey for in-context learning,” arXiv preprint , 2022
2022
Cited alongside, same era.
E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “GPTQ: Accurate post-training quantization for generative pre-trained transformers,” arXiv preprint , 2022
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” in Proc. NIPS , 2022
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing et al. , “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” arXiv preprint , 2023
2023
Closest in time.
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “QLoRA: Efficient finetuning of quantized LLMs,” arXiv preprint , 2023
2023
Closest in time.
S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, and A. Awadallah, “Orca: Progressive learning from complex explanation traces of GPT-4,” arXiv preprint , 2023
2023
Closest in time.
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” arXiv preprint , 2023
2023
Closest in time.
T. Auto-GPT, “Auto-GPT: An Autonomous GPT-4 Experiment,” https://github.com/Significant-Gravitas/Auto-GPT , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. H. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” in Proc. NIPS , 2022
2022
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large Language Models are Zero-Shot Reasoners,” in Proc. NIPS , 2022
2022
Cited alongside, same era.
“iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library, author=Li, Xingquan and others,” in International Symposium of Electronics Design Automation (ISEDA) , 2023
2023
Cited alongside, same era.
OpenAI, “GPT-4 Technical Report,” 2023
2023
Cited alongside, same era.
T. Anthropic, “Claude,” https://www.anthropic.com/ , 2023
2023
Cited alongside, same era.
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar et al. , “Llama: Open and efficient foundation language models,” arXiv preprint , 2023
2023
Cited alongside, same era.
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale et al. , “Llama 2: Open Foundation and Fine-Tuned Chat Models,” arXiv preprint , 2023
2023
Cited alongside, same era.
2023
Closest in time.
T. BabyAGI, “BabyAGI,” https://github.com/yoheinakajima/babyagi , 2023
2023
Closest in time.
S. G. Patil, T. Zhang, X. Wang, and J. E. Gonzalez, “Gorilla: Large language model connected with massive APIs,” arXiv preprint , 2023
2023
Closest in time.
R. Anil, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen et al. , “Palm 2 technical report,” arXiv preprint , 2023
2023
Closest in time.
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin et al. , “Code llama: Open foundation models for code,” arXiv preprint , 2023
2023
Closest in time.
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford Alpaca: An Instruction-following LLaMA model,” https://github.com/tatsu-lab/stanford_alpaca , 2023
2023
Closest in time.
T. Dettmers and L. Zettlemoyer, “The case for 4-bit precision: k-bit inference scaling laws,” in Proc. ICML , 2023
2023
Closest in time.
Z. Luo, C. Xu, P. Zhao, Q. Sun, X. Geng, W. Hu, C. Tao, J. Ma, Q. Lin, and D. Jiang, “WizardCoder: Empowering Code Large Language Models with Evol-Instruct,” arXiv preprint , 2023
2023
Closest in time.
H. Su, J. Kasai, Y. Wang, Y. Hu, M. Ostendorf, W.-t. Yih, N. A. Smith, L. Zettlemoyer, T. Yu et al. , “One embedder, any task: Instruction-finetuned text embeddings,” Proc. ACL , 2023
2023
Closest in time.
Z. He, H. Wu, X. Zhang, X. Yao, S. Zheng, H. Zheng, and B. Yu, “ChatEDA: A Large Language Model Powered Autonomous Agent for EDA,” in Proc. MLCAD , 2023
2023
Closest in time.