From: <gat...@us...> - 2024-07-12 15:26:28
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Revision: 20405 http://sourceforge.net/p/gate/code/20405 Author: gate-project Date: 2024-07-12 15:26:26 +0000 (Fri, 12 Jul 2024) Log Message: ----------- [yidamu] Edited with GATEWiki Modified Paths: -------------- gate/trunk/doc/papers.html gate/trunk/doc/papers.yam Modified: gate/trunk/doc/papers.html =================================================================== --- gate/trunk/doc/papers.html 2024-07-05 09:41:48 UTC (rev 20404) +++ gate/trunk/doc/papers.html 2024-07-12 15:26:26 UTC (rev 20405) @@ -108,20 +108,24 @@ <h3 class="cow-heading">Conference papers</h3> <ul> -<li>Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song. Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling LREC-COLING 2024</li> +<li>Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song. Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling LREC-COLING 2024 <a class="cow-url" href="https://aclanthology.org/2024.lrec-main.887.pdf">https://aclanthology.org/2024.lrec-main.887.pdf</a></li> </ul> <ul> -<li>Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song. Examining Temporalities on Stance Detection Towards COVID-19 Vaccination. LREC-COLING 2024 <a class="cow-url" href="https://arxiv.org/abs/2304.04806">https://arxiv.org/abs/2304.04806</a></li> +<li>Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song. Examining Temporalities on Stance Detection Towards COVID-19 Vaccination. LREC-COLING 2024 <a class="cow-url" href="https://aclanthology.org/2024.lrec-main.594/">https://aclanthology.org/2024.lrec-main.594/</a></li> </ul> <ul> -<li>Yida Mu, Xingyi Song, Kalina Bontcheva, and Nikolaos Aletras. Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets. LREC-COLING 2024 <a class="cow-url" href="https://arxiv.org/abs/2309.11576">https://arxiv.org/abs/2309.11576</a></li> +<li>Yida Mu, Xingyi Song, Kalina Bontcheva, and Nikolaos Aletras. Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets. LREC-COLING 2024 <a class="cow-url" href="https://aclanthology.org/2024.lrec-main.595.pdf">https://aclanthology.org/2024.lrec-main.595.pdf</a></li> </ul> <ul> -<li>Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song. Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science. LREC-COLING 2024 <a class="cow-url" href="https://arxiv.org/abs/2305.14310">https://arxiv.org/abs/2305.14310</a></li> +<li>Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song. Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science. LREC-COLING 2024 <a class="cow-url" href="https://aclanthology.org/2024.lrec-main.1055/">https://aclanthology.org/2024.lrec-main.1055/</a></li> </ul> +<h3 class="cow-heading">Pre-print</h3> +<ul> +<li>Y Mu, P Bai, K Bontcheva, X Song. (2024) Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling <a class="cow-url" href="https://arxiv.org/pdf/2405.00611">https://arxiv.org/pdf/2405.00611</a></li> +</ul> <h2 class="cow-heading">2023</h2><h3 class="cow-heading">Journal papers</h3> <ul> <li>Ye Jiang; Xiaomin Yu; Yimin Wang; Xiaoman Xu; Xingyi Song; Diana Maynard. Similarity-Aware Multimodal Prompt Learning for Fake News Detection. Journal of Information Sciences (pre-print) <a class="cow-url" href="https://www.sciencedirect.com/science/article/abs/pii/S0020025523010319">https://www.sciencedirect.com/science/article/abs/pii/S0020025523010319</a></li> Modified: gate/trunk/doc/papers.yam =================================================================== --- gate/trunk/doc/papers.yam 2024-07-05 09:41:48 UTC (rev 20404) +++ gate/trunk/doc/papers.yam 2024-07-12 15:26:26 UTC (rev 20405) @@ -53,14 +53,17 @@ %3* Conference papers -- Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song. Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling LREC-COLING 2024 +- Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song. Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling LREC-COLING 2024 %(https://aclanthology.org/2024.lrec-main.887.pdf) -- Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song. Examining Temporalities on Stance Detection Towards COVID-19 Vaccination. LREC-COLING 2024 %(https://arxiv.org/abs/2304.04806) +- Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song. Examining Temporalities on Stance Detection Towards COVID-19 Vaccination. LREC-COLING 2024 %(https://aclanthology.org/2024.lrec-main.594/) -- Yida Mu, Xingyi Song, Kalina Bontcheva, and Nikolaos Aletras. Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets. LREC-COLING 2024 %(https://arxiv.org/abs/2309.11576) +- Yida Mu, Xingyi Song, Kalina Bontcheva, and Nikolaos Aletras. Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets. LREC-COLING 2024 %(https://aclanthology.org/2024.lrec-main.595.pdf) -- Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song. Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science. LREC-COLING 2024 %(https://arxiv.org/abs/2305.14310) +- Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song. Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science. LREC-COLING 2024 %(https://aclanthology.org/2024.lrec-main.1055/) +%3* Pre-print +- Y Mu, P Bai, K Bontcheva, X Song. (2024) Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling %(https://arxiv.org/pdf/2405.00611) + %2* 2023 %3* Journal papers - Ye Jiang; Xiaomin Yu; Yimin Wang; Xiaoman Xu; Xingyi Song; Diana Maynard. Similarity-Aware Multimodal Prompt Learning for Fake News Detection. Journal of Information Sciences (pre-print) %(https://www.sciencedirect.com/science/article/abs/pii/S0020025523010319) This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. |