Fetching the paper…

Reading the bibliography…

2025

First Token Probability Guided RAG for Telecom Question Answering

Chen, Tingwei, Chen, Jiayi, Zhao, Zijian et al.

Original
PDF
Post on X

Understand

Large Language Models (LLMs) have garnered significant attention for their impressive general-purpose capabilities.

For applications requiring intricate domain knowledge, Retrieval-Augmented Generation (RAG) has shown a distinct advantage in incorporating domain-specific information into LLMs.
However, existing RAG research has not fully addressed the challenges of Multiple Choice Question Answering (MCQA) in telecommunications, particularly in terms of retrieval quality and mitigating hallucinations.
To tackle these challenges, we propose a novel first token probability guided RAG framework.

First Token Probability Guided RAG for Telecom Question Answering · Around

Built on

V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, “Dense passage retrieval for open-domain question answering,” arXiv preprint arXiv:2004.04906 , 2020
Original
2020
Earlier work this paper cites.
H. Li, Y. Su, D. Cai, Y. Wang, and L. Liu, “A survey on retrieval-augmented text generation,” arXiv preprint arXiv:2202.01110 , 2022
Original
2022
Earlier work this paper cites.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong et al. , “A survey of large language models,” arXiv preprint arXiv:2303.18223 , 2023
Original
2023
Earlier work this paper cites.
S. Ouyang, J. M. Zhang, M. Harman, and M. Wang, “Llm is like a box of chocolates: the non-determinism of chatgpt in code generation,” arXiv preprint arXiv:2308.02828 , 2023
Original
2023
Earlier work this paper cites.
J. Robinson, C. M. Rytting, and D. Wingate, “Leveraging large language models for multiple choice question answering,” 2023. [Online]. Available: https://arxiv.org/abs/2210.12353
Original
2023
Earlier work this paper cites.
H. Zhou, C. Hu, Y. Yuan, Y. Cui, Y. Jin, C. Chen, H. Wu, D. Yuan, L. Jiang, D. Wu et al. , “Large language model (llm) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities,” arXiv preprint arXiv:2405.10825 , 2024
Original
2024
Earlier work this paper cites.

Similar

H. Chen, H. Chen, Z. Zhao, K. Han, G. Zhu, Y. Zhao, Y. Du, W. Xu, and Q. Shi, “An overview of domain-specific foundation model: key technologies, applications and challenges,” arXiv preprint arXiv:2409.04267 , 2024
Original
2024
Cited alongside, same era.
Y. Wu, F. Jia, S. Zhang, H. Li, E. Zhu, Y. Wang, Y. T. Lee, R. Peng, Q. Wu, and C. Wang, “Mathchat: Converse to tackle challenging math problems with llm agents,” in ICLR 2024 Workshop on Large Language Model (LLM) Agents , 2024
2024
Cited alongside, same era.
X. Liang, Z. Zhao, W. Zeng, Y. He, F. He, Y. Wang, and C. Gao, “Pianobart: Symbolic piano music generation and understanding with large-scale pre-training,” arXiv preprint arXiv:2407.03361 , 2024
Original
2024
Cited alongside, same era.
D. Edge, H. Trinh, N. Cheng, J. Bradley, A. Chao, A. Mody, S. Truitt, and J. Larson, “From local to global: A graph rag approach to query-focused summarization,” arXiv preprint arXiv:2404.16130 , 2024
Original
2024
Cited alongside, same era.

Then

G. M. Yilma, J. A. Ayala-Romero, A. Garcia-Saavedra, and X. Costa-Perez, “Telecomrag: Taming telecom standards with retrieval augmented generation and llms,” arXiv preprint arXiv:2406.07053 , 2024
Original
2024
Later among the works it cites.
X. Wang, C. Hu, B. Ma, P. Röttger, and B. Plank, “Look at the text: Instruction-tuned language models are more robust multiple choice selectors than you think,” arXiv preprint arXiv:2404.08382 , 2024
Original
2024
Later among the works it cites.
T. Ahmed, N. Piovesan, A. De Domenico, and S. Choudhury, “Linguistic intelligence in large language models for telecommunications,” arXiv preprint arXiv:2402.15818 , 2024
Original
2024
Later among the works it cites.
N. Piovesan, A. De Domenico, and F. Ayed, “Telecom language models: Must they be large?” arXiv preprint arXiv:2403.04666 , 2024
Original
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Y. Yu, W. Ping, Z. Liu, B. Wang, J. You, C. Zhang, M. Shoeybi, and B. Catanzaro, “Rankrag: Unifying context ranking with retrieval-augmented generation in llms,” arXiv preprint arXiv:2407.02485 , 2024

2024

Cited alongside, same era.

A.-L. Bornea, F. Ayed, A. De Domenico, N. Piovesan, and A. Maatouk, “Telco-rag: Navigating the challenges of retrieval-augmented language models for telecommunications,” arXiv preprint arXiv:2404.15939 , 2024

2024

Cited alongside, same era.

“Specializing Large Language Models for Telecom Networks by ITU AI/ML in 5G Challenge,” [Online]. Available: https://zindi.africa/competitions/specializing-large-language-models-for-telecom-networks, 2024

2024

Later among the works it cites.

M. Douze, A. Guzhva, C. Deng, J. Johnson, G. Szilvasy, P.-E. Mazaré, M. Lomeli, L. Hosseini, and H. Jégou, “The faiss library,” 2024

2024

Later among the works it cites.