Jinyuan Fang Profile
Jinyuan Fang

@JinyuanF

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149
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26

PhD student at University of Glasgow

Joined January 2017
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@JinyuanF
Jinyuan Fang
@JinyuanF
8 days
RT @XinhaoYi: 🚀 Excited to share that our paper "BPP: A Platform for Automatic Biochemical Pathway Prediction" (w/ @TedSiwei, Yu Wu, Dougla…
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@JinyuanF
Jinyuan Fang
@JinyuanF
28 days
RT @keirworkshop: 📢 Excited to announce that Prof. Alessandro Lenci (@AlexLenci1966) from @Unipisa and Dr. Andrew Yates (@andrewyates) from…
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@JinyuanF
Jinyuan Fang
@JinyuanF
3 months
RT @keirworkshop: 📢 We are excited to announce the Call for Papers for the 2nd KEIR at @ecir2025 #ECIR2025! 📅 Submission Deadline: Janua…
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@JinyuanF
Jinyuan Fang
@JinyuanF
3 months
RT @ZixuanYI_: At 15:00 on 17th November, Aixin Sun @AixinSG from the Nanyang Technological University will give an #IRTalk entitled "Under…
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@JinyuanF
Jinyuan Fang
@JinyuanF
3 months
RT @JackMcK1999: Today, Jinyuan Fang @JinyuanF from the University of Glasgow is giving an #IRTalk entitled "Knowledge Graph Enhanced Retri…
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@JinyuanF
Jinyuan Fang
@JinyuanF
4 months
RT @keirworkshop: Excited to share that the 2nd KEIR workshop will be held at @ecir2025 #ECIR2025. 🎉 Huge thanks to @ZihanWa54274484 @Jiny…
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@JinyuanF
Jinyuan Fang
@JinyuanF
5 months
🎉Glad to share that our paper "TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation"(w/ @mengzaiqiao and @craig_macdonald ) has been accepted at #EMNLP2024 @emnlpmeeting as a findings paper!
@JinyuanF
Jinyuan Fang
@JinyuanF
7 months
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation (w/ @mengzaiqiao, @craig_macdonald) Takeaway: using reasoning chains (purely KG triples) built from docs beats using full docs for RAGs. #rag Paper:
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@JinyuanF
Jinyuan Fang
@JinyuanF
6 months
Some of us from Glasgow will be presenting our work at #ACL2024NLP! Come by to chat about our latest research in NLP and BioNLP. @ir_glasgow
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@JinyuanF
Jinyuan Fang
@JinyuanF
7 months
RT @tjaenich: I can't wait to meet everyone in Washington D.C.!🥳 I'll be speaking in the Fairness session on Tuesday and presenting a poste…
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@JinyuanF
Jinyuan Fang
@JinyuanF
7 months
4. Experimental results show TRACE achieves up to 14.03% performance improvement over using all retrieved documents. Moreover, using reasoning chains as context, rather than the entire documents, is often sufficient to correctly answer questions. Code:
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@JinyuanF
Jinyuan Fang
@JinyuanF
9 months
I am glad to share that our full paper "REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation" (w/ @mengzaiqiao and @craig_macdonald) has been accepted to #ACL2024 main conference!
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@JinyuanF
Jinyuan Fang
@JinyuanF
11 months
RT @keirworkshop: Listening to @ridhorei delivering his keynote entitled "Untilizing Structured and Encoded Knowledge for Search". Come and…
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@JinyuanF
Jinyuan Fang
@JinyuanF
11 months
RT @keirworkshop: 🌟Exciting News! The KEIR Workshop at @ecir2024 will starts tomorrow! #KEIR #ecir2024 ⏰Don't miss out! Check the schedu…
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@JinyuanF
Jinyuan Fang
@JinyuanF
1 year
RT @ecir2024: Just back from a site visit to the #ecir2024 conference venue @RadissonBlu Glasgow - less than 10 weeks to go!
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@JinyuanF
Jinyuan Fang
@JinyuanF
1 year
RT @keirworkshop: 📢 Deadline Extension! The submission deadline has been extended to: January 26, 2024, 11:59pm (AOE). Don’t miss the chan…
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@JinyuanF
Jinyuan Fang
@JinyuanF
1 year
RT @keirworkshop: ⏰Time is ticking! The deadline for workshop paper submissions is approaching fast. Make sure to finalise and submit your…
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