Thema5026: Unterschied zwischen den Versionen
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|Nachname=Liu | |Nachname=Liu | ||
|Abschlussarbeitstyp=Master | |Abschlussarbeitstyp=Master | ||
| − | |Betreuer=Jin Liu | + | |Betreuer=Jin Liu |
|Partner=FZI Forschungszentrum Informatik | |Partner=FZI Forschungszentrum Informatik | ||
|Forschungsgruppe=Web Science | |Forschungsgruppe=Web Science | ||
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|Beginn=2023/04/11 | |Beginn=2023/04/11 | ||
|Ausschreibung=Multihop-Retrieval_Reasoning.pdf | |Ausschreibung=Multihop-Retrieval_Reasoning.pdf | ||
| − | |Beschreibung DE=The spread of fake news has caused more and more concern in the public. Reliable information retrieval is essential for fact-checking. Multi-hop information retrieval requires the extraction of information from several sources. The reasoning step processes the retrieved information and verifies the corresponding claim. In this thesis, we focus on the HOVER dataset with pre-trained large language models. | + | |Beschreibung DE=The spread of fake news has caused more and more concern in the public. Reliable information retrieval is essential for fact-checking. Multi-hop information retrieval requires the extraction of information from several sources. The reasoning step processes the retrieved information and verifies the corresponding claim. In this thesis, we focus on the HOVER dataset with pre-trained large language models. |
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Aktuelle Version vom 5. April 2023, 11:31 Uhr
Multi-hop Retrieval and Reasoning with Language Models for Fake News Detection
Informationen zur Arbeit
Abschlussarbeitstyp: Master
Betreuer: Jin Liu
Forschungsgruppe: Web Science
Partner: FZI Forschungszentrum Informatik
Archivierungsnummer: 5026
Abschlussarbeitsstatus: Offen
Beginn:
11. April 2023
Abgabe: unbekannt
Weitere Informationen
The spread of fake news has caused more and more concern in the public. Reliable information retrieval is essential for fact-checking. Multi-hop information retrieval requires the extraction of information from several sources. The reasoning step processes the retrieved information and verifies the corresponding claim. In this thesis, we focus on the HOVER dataset with pre-trained large language models.
Ausschreibung: Download (pdf)