Open Access
| Issue |
EPJ Web Conf.
Volume 370, 2026
International Conference on Advanced Physics: Innovations for a Sustainable Future (IEMPHYS-26)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/epjconf/202637001014 | |
| Published online | 29 May 2026 | |
- P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.Y. Wang, Y. Stoyanov, Retrieval-augmented generation for knowledgeintensive NLP tasks. Adv. Neural Inf. Process. Syst. 33, 9459-9474 (2020) [Google Scholar]
- Y. Gao et al., Retrieval-augmented generation for large language models: A survey. arXiv:2312.10997 2023 [Google Scholar]
- P.A. Olujimi, A. Ade-Ibijola, NLP techniques for automating responses to customer queries: A systematic review. Discov. Artif. Intell. 3, 20 (2023) [Google Scholar]
- F.R. Rusch et al., Context is all you need: Enhancing contextual awareness on sustainability requirements in product development using natural language processing. Procedia CIRP 135, 1052-1057 (2025) [Google Scholar]
- G. Agrawal, S. Gummuluri, C. Spera, Beyond-RAG: Question identification and answer generation in real-time conversations. arXiv:2410.10136 2024 [Google Scholar]
- J. Benita et al., Implementation of retrieval-augmented generation (RAG) in chatbot systems for enhanced real-time customer support in e-commerce, in Proc. 3rd Int. Conf, on Automation, Computing and Renewable Systems (ICACRS), IEEE 2024 [Google Scholar]
- R. Khanna, S. Bhagat, Revolutionizing customer support: The impact of AI-powered chatbots, in Proc. Int. Conf. on Computational Intelligence and Communication Technology (ICCICT), 101-107 2024 [Google Scholar]
- Z. Zhang, S. Zhang, H. Zhang, H. Zhao, M. Zhou, LLM-Eval: Unified multidimensional automatic evaluation for open-domain conversations, in Proc. 62nd Annu. Meet. Assoc. Comput. Linguist. (ACL), 12234-12249 2024 [Google Scholar]
- S. Goyal, P. Sharma, R. Verma, Context is all you need: Enhancing contextual awareness on sustainability reporting using retrieval-augmented generation. Procedia CIRP 126, 505-510 (2023) [Google Scholar]
- L. Wang, H. Zhao, Y. Chen, Carbon footprint accounting driven by large language models and retrieval-augmented generation. Sustain. Comput. Inform. Syst. 42, 100812 (2024) [Google Scholar]
- J. Wu, H. Tang, X. Li, An efficient memory-augmented transformer for knowledgeintensive NLP tasks. Expert Syst. Appl. 236, 121102 (2025) [Google Scholar]
- X. Yu et al., Report Friendly: An interface design for an LLM-empowered ESG report generation system, in Int. Conf. on Human-Computer Interaction (Springer Nature Switzerland, Cham, 2025) [Google Scholar]
- J.-Y. Yang et al., EcoSmartGuide: Language learning model and retrieval-augmented generation-based platform for streamlined environmental, social, and governance information access and report generation, in 2024 IEEE 6th Eurasia Conf. on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), IEEE 2024 [Google Scholar]
- C. He et al., ESGenius: Benchmarking LLMs on environmental, social, and governance (ESG) and sustainability knowledge. arXiv:2506.01646 2025 [Google Scholar]
- K. Karia et al., Leveraging large language models for evaluating customer service conversations and retrieval-augmented generation for pre-call insights, in 2024 Int. Conf, on Communication, Control, and Intelligent Systems (CCIS), IEEE 2024 [Google Scholar]
- S. Veturi et al., RAG-based question-answering for contextual response prediction system. arXiv:2409, 03708 (2024) [Google Scholar]
- D. Garigliotti, SDG target detection in environmental reports using retrieval-augmented generation with LLMs, in Proc. 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP2024) 2024 [Google Scholar]
- J. Hinrichs et al., LLM-powered chatbot for managerial sustainability insights, in EnviroInfo 2024, Gesellschaft für Informatik e.V. 2024 [Google Scholar]
- Y. Zou et al., ESGReveal: An LLM-based approach for extracting structured data from ESG reports. J. Clean. Prod. 489, 144572 (2025) [Google Scholar]
- S. Yao et al., React: Synergizing reasoning and acting in language models, in Int. Conf. on Learning Representations (ICLR) 2022 [Google Scholar]
- Y. Bai et al., Constitutional AI: Harmlessness from AI feedback. arXiv:2212.08073 2022 [Google Scholar]
- Z. Gou et al., Critic: Large language models can self-correct with tool-interactive critiquing. arXiv:2305.11738 2023 [Google Scholar]
- A. Asai et al., Self-RAG: Learning to retrieve, generate, and critique through selfreflection 2024 [Google Scholar]
- L. Zheng et al., Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. Adv. Neural Inf. Process. Syst. 36, 46595-46623 (2023) [Google Scholar]
- Y. Liu et al., G-Eval: NLG evaluation using GPT-4 with better human alignment. arXiv:2303.16634 2023 [Google Scholar]
- T. Schick et al., Toolformer: Language models can teach themselves to use tools. arXiv:2302.04761 2023 [Google Scholar]
- Z. Xi et al., A survey on large language model based autonomous agents. arXiv:2308.11432 2023 [Google Scholar]
- M. Alam, S.S. Ahmad, S.A. Khan, A. Rahman, Increasing customer service efficiency through artificial intelligence chatbots, in Proc. IEEE Int. Conf. on Smart Technologies, 211-217 2024 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

