mlsanityData to 7 Oct 2026

Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications

arXiv 2308.04033 ↗First posted 8 Aug 2023cs.NI cs.AI cs.IRcited by 7

Not on the map. Why?

Abstract (arXiv)

Existing approaches to understanding, developing and researching modern wireless communication technologies involves time-intensive and arduous process of sifting through numerous webpages and technical specification documents, gathering the required information and synthesizing it. This paper presents NextGen Communications Copilot, a conversational artificial intelligence tool for information synthesis of wireless communication specifications. The system builds on top of recent advancements in foundation models and consists of three key additional components: a domain-specific database, a context extractor, and a feedback mechanism. The system appends user queries with concise and query-dependent contextual information extracted from a database of wireless technical specifications and incorporates tools for expert feedback and data contributions. On evaluation using a benchmark dataset of queries and reference responses created by subject matter experts, the system demonstrated more relevant and accurate answers with an average BLEU score and BERTScore F1-measure of 0.37 and 0.79 respectively compared to the corresponding values of 0.07 and 0.59 achieved by state-of-the-art tools like ChatGPT.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 19 of its 56 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

19 of 56 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  6. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  7. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  8. 2017Billion-scale similarity search with GPUscited by 2,477
  9. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  10. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  11. 2023Textbooks Are All You Needcited by 561
  12. 2023Augmented Language Models: a Surveycited by 398
7 more
  1. 2022Ask Me Anything: A simple strategy for prompting language modelscited by 155
  2. 2022DocPrompting: Generating Code by Retrieving the Docscited by 151
  3. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  4. 2019Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classificationcited by 62
  5. 2023Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answeringcited by 21
  6. 2023SPEC5G: A Dataset for 5G Cellular Network Protocol Analysiscited by 20
  7. 2020Semi-Automated Protocol Disambiguation and Code Generationcited by 9

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 2.

7 papers.