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Found 8 translations.
| Name | Current message text |
|---|---|
| h German (de) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12 rag pain points and proposed solutions. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. Active retrieval augmented generation. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., and Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h English (en) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12 rag pain points and proposed solutions. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. Active retrieval augmented generation. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., and Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h Spanish (es) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12 rag pain points and proposed solutions. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. Active retrieval augmented generation. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., and Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h French (fr) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. Rapport technique GPT-4. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., et Abdelrazek, M. Sept points de défaillance lors de l'ingénierie d'un système de génération augmentée par récupération. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., et Schockaert, S. Ragas : Évaluation automatisée de la génération augmentée par récupération. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''Développement de chatbots d'entreprise''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12 points de douleur RAG et solutions proposées. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., et Neubig, G. Génération augmentée par récupération active. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Génération augmentée par récupération pour les tâches NLP intensives en connaissances. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo : LLMs adaptés au domaine pour la conception de puces. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Auto-affinage : Affinage itératif avec auto-feedback. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Modèles de langage augmentés : une enquête. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., et Cohen, J. Nemo guardrails : Un kit d'outils pour des applications LLM contrôlables et sûres avec des rails programmables. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., et Zaharia, M. Ares : Un cadre d'évaluation automatisé pour les systèmes de génération augmentée par récupération. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., et Vidra, N. Amélioration de la récupération pour les modèles de réponse aux questions basés sur RAG sur les documents financiers. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., et Cao, Y. React : Synergiser le raisonnement et l'action dans les modèles de langage. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., et Wen, J.-R. Grands modèles de langage pour la récupération d'informations : une enquête. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h Japanese (ja) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., 他. GPT-4技術報告. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. 検索強化生成システムのエンジニアリングにおける7つの失敗点. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: 検索強化生成の自動評価. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''エンタープライズチャットボットの開発''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12のRAGの痛点と提案された解決策. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. アクティブ検索強化生成. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., 他. 知識集約型NLPタスクのための検索強化生成. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., 他. Chipnemo: チップ設計のためのドメイン適応型LLM. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., 他. Self-refine: 自己フィードバックによる反復的洗練. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., 他. 拡張言語モデル: 調査. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemoガードレール: プログラム可能なレールを備えた制御可能で安全なLLMアプリケーションのためのツールキット. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: 検索強化生成システムのための自動評価フレームワーク. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., and Vidra, N. 金融文書におけるRAGベースの質問応答モデルの検索改善. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: 言語モデルにおける推論と行動のシナジー. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. 情報検索のための大規模言語モデル: 調査. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h Korean (ko) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 기술 보고서. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. 검색 증강 생성 시스템을 설계할 때의 일곱 가지 실패 지점. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: 검색 증강 생성의 자동 평가. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''기업용 챗봇 개발''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12개의 rag 문제점과 제안된 해결책. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. 능동적 검색 증강 생성. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. 지식 집약적 NLP 작업을 위한 검색 증강 생성. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: 칩 설계를 위한 도메인 적응 LLM. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: 자기 피드백을 통한 반복적 정제. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. 증강 언어 모델: 설문 조사. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: 프로그래머블 레일을 사용한 제어 가능하고 안전한 LLM 응용 프로그램을 위한 도구 키트. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: 검색 증강 생성 시스템을 위한 자동 평가 프레임워크. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., and Vidra, N. 금융 문서에 대한 RAG 기반 질문 응답 모델의 검색 개선. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: 언어 모델에서 추론과 행동의 시너지. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. 정보 검색을 위한 대형 언어 모델: 설문 조사. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h Portuguese (pt) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., e Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., e Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12 rag pain points and proposed solutions. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., e Neubig, G. Active retrieval augmented generation. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., e Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., e Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., e Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., e Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., e Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023). |
| h Chinese (zh) | * <span id="bib.bib1">(1)</span> Langchain. ''https://github.com/langchain-ai''. * <span id="bib.bib2">(2)</span> Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. ''arXiv preprint arXiv:2303.08774'' (2023). * <span id="bib.bib3">(3)</span> Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., and Abdelrazek, M. Seven failure points when engineering a retrieval augmented generation system. ''arXiv preprint arXiv:2401.05856'' (2024). * <span id="bib.bib4">(4)</span> Es, S., James, J., Espinosa-Anke, L., and Schockaert, S. Ragas: Automated evaluation of retrieval augmented generation. ''arXiv preprint arXiv:2309.15217'' (2023). * <span id="bib.bib5">(5)</span> Galitsky, B. ''Developing enterprise chatbots''. Springer, 2019. * <span id="bib.bib6">(6)</span> Glantz, W. 12 rag pain points and proposed solutions. * <span id="bib.bib7">(7)</span> Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., and Neubig, G. Active retrieval augmented generation. ''arXiv preprint arXiv:2305.06983'' (2023). * <span id="bib.bib8">(8)</span> Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ''Advances in Neural Information Processing Systems 33'' (2020), 9459–9474. * <span id="bib.bib9">(9)</span> Liu, J. LlamaIndex. ''https://github.com/jerryjliu/llama_index''(2022). * <span id="bib.bib10">(10)</span> Liu, M., Ene, T.-D., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, I., et al. Chipnemo: Domain-adapted llms for chip design. ''arXiv preprint arXiv:2311.00176'' (2023). * <span id="bib.bib11">(11)</span> Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. Self-refine: Iterative refinement with self-feedback. ''Advances in Neural Information Processing Systems 36'' (2024). * <span id="bib.bib12">(12)</span> Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al. Augmented language models: a survey. ''arXiv preprint arXiv:2302.07842'' (2023). * <span id="bib.bib13">(13)</span> Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. ''arXiv preprint arXiv:2310.10501'' (2023). * <span id="bib.bib14">(14)</span> Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M. Ares: An automated evaluation framework for retrieval-augmented generation systems. ''arXiv preprint arXiv:2311.09476'' (2023). * <span id="bib.bib15">(15)</span> Setty, S., Jijo, K., Chung, E., and Vidra, N. Improving retrieval for rag based question answering models on financial documents. ''arXiv preprint arXiv:2404.07221'' (2024). * <span id="bib.bib16">(16)</span> Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. React: Synergizing reasoning and acting in language models. ''arXiv preprint arXiv:2210.03629'' (2022). * <span id="bib.bib17">(17)</span> Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Dou, Z., and Wen, J.-R. Large language models for information retrieval: A survey. ''arXiv preprint arXiv:2308.07107'' (2023). |