cise-midoglu commited on
Commit
8bf84c0
1 Parent(s): 4ff8cab

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +12 -12
README.md CHANGED
@@ -1,16 +1,8 @@
1
  # SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries
2
 
3
  ## Abstract
4
- The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper intro-
5
- duces SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large
6
- Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal
7
- dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to
8
- sports archives. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements
9
- over traditional retrieval systems in terms of accuracy and
10
- user engagement. The results underscore the potential of using
11
- RAG and LLMs in sports analytics, paving the way for future
12
- advancements in the accessibility and real-time processing of
13
- sports data.
14
  ## Setup
15
  ````bash
16
  pip install -r requirements.txt
@@ -59,8 +51,16 @@ Lionel Messi has scored the following number of goals each season:
59
  ![result-table.png](media%2Fresult-table.png)
60
 
61
  ## Acknowledgements
62
- ..
63
 
64
  ## Citation
65
- ..
 
 
 
 
 
 
 
 
66
 
 
1
  # SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries
2
 
3
  ## Abstract
4
+ The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This work introduces SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements over traditional retrieval systems in terms of accuracy and user engagement. The results underscore the potential of using RAG and LLMs in sports analytics, paving the way for future advancements in the accessibility and real-time processing of sports data.
5
+
 
 
 
 
 
 
 
 
6
  ## Setup
7
  ````bash
8
  pip install -r requirements.txt
 
51
  ![result-table.png](media%2Fresult-table.png)
52
 
53
  ## Acknowledgements
54
+ This research was partly funded by the Research Council of Norway, project number 346671 ([AI-storyteller](https://prosjektbanken.forskningsradet.no/project/FORISS/346671)).
55
 
56
  ## Citation
57
+ ```
58
+ @incollection{Strand2024,
59
+ author = {Aleksander Theo Strand et al.},
60
+ title = {{SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries}},
61
+ booktitle = {{CBMI 2024: 21st International Conference on Content-Based Multimedia Indexing (under review)}},
62
+ year = {2024},
63
+ publisher = {IEEE}
64
+ }
65
+ ```
66