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Recently, large language models (LLMs), such as GPT-4, stand out remarkable conversational abilities, enabling them to engage in dynamic and contextually relevant dialogues across a wide range of topics.
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Language models are few-shot learners
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A diversity-promoting objective function for neural conversation models, in: Annual Conference of the North American Chapter of the Association for Computational Linguistics
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How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation, in: the Conference on Empirical Methods in Natural Language Processing
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Personalizing dialogue agents: I have a dog, do you have pets too?, in: the Annual Meeting of the Association for Computational Linguistics
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Survey on evaluation methods for dialogue systems
Deriu, J., Rodrigo, Á., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., Cieliebak, M., 2019 · 2019
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Retrieval augmented language model pre-training, in: the International Conference on Learning Representations
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Dense passage retrieval for open-domain question answering, in: the Conference on Empirical Methods in Natural Language Processing
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Retrieval-augmented generation for knowledge-intensive nlp tasks, in: the Annual Conference on Neural Information Processing Systems
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Keep me updated! memory management in long-term conversations, in: Findings of the Conference on Empirical Methods in Natural Language Processing
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Dao, T., Fu, D., Ermon, S., Rudra, A., Ré, C., 2022 · 2022
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Open-domain dialogue generation: What we can do, cannot do, and should do next, in: NLP4CONVAI
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Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage
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Memformer: A memory-augmented transformer for sequence modeling, in: Findings of the Annual Meeting of the Association for Computational Linguistics
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Lost in the middle: How language models use long contexts, in: Transactions of the Association for Computational Linguistics
Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P., 2023 · 2023
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Do the rewards justify the means? measuring trade-offs between rewards and ethical behavior in the machiavelli benchmark, in: the International Conference on Machine Learning
Pan, A., Shern, C.J., Zou, A., Li, N., Basart, S., Woodside, T., Ng, J., Zhang, H., Emmons, S., Hendrycks, D., 2023 · 2023
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Towards making the most of chatgpt for machine translation
Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., Ouyang, Y., Tao, D., 2023 · 2023
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Summarization is (almost) dead
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History-aware hierarchical transformer for multi-session open-domain dialogue system, in: Findings of the Conference on Empirical Methods in Natural Language Processing
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L-eval: Instituting standardized evaluation for long context language models
An, C., Gong, S., Zhong, M., Li, M., Zhang, J., Kong, L., Qiu, X., 2023 · 2023
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Extending context window of large language models via positional interpolation
Chen, S., Wong, S., Chen, L., Tian, Y., 2023 · 2023
Cited alongside, same era.
Effortless integration of memory management into open-domain conversation systems
Choi, E., On, K.W., Han, G., Kim, S., Nam, D.W., Jo, D., Rho, S.E., Kwon, T., Seo, M., 2023 · 2023
Cited alongside, same era.
Prompted llms as chatbot modules for long open-domain conversation, in: Findings of the Annual Meeting of the Association for Computational Linguistics
Lee, G., Hartmann, V., Park, J., Papailiopoulos, D., Lee, K., 2023 · 2023
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Loogle: Can long-context language models understand long contexts?
Li, J., Wang, M., Zheng, Z., Zhang, M., 2023 · 2023
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Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al., 2023 · 2023
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Chatgpt or grammarly? evaluating chatgpt on grammatical error correction benchmark
Wu, H., Wang, W., Wan, Y., Jiao, W., Lyu, M., 2023 · 2023
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Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert
Zhong, Q., Ding, L., Liu, J., Du, B., Tao, D., 2023 · 2023
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Facilitating multi-turn emotional support conversation with positive emotion elicitation: A reinforcement learning approach, in: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1714–1729
Zhou, J., Chen, Z., Wang, B., Huang, M., 2023 · 2023
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Alpacafarm: A simulation framework for methods that learn from human feedback
Dubois, Y., Li, C.X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., Guestrin, C., Liang, P.S., Hashimoto, T.B., 2024 · 2024
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Chatglm: A family of large language models from glm-130b to glm-4 all tools
GLM, T., Zeng, A., Xu, B., Wang, B., Zhang, C., Yin, D., Rojas, D., Feng, G., Zhao, H., Lai, H., et al., 2024 · 2024
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Long-range language modeling with self-retrieval, in: Transactions of the Association for Computational Linguistics
Rubin, O., Berant, J., 2024 · 2024
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Memorybank: Enhancing large language models with long-term memory, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 19724–19731
Zhong, W., Guo, L., Gao, Q., Ye, H., Wang, Y., 2024 · 2024
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