bib

README.md

@@ -86,19 +86,8 @@ This metric is based on an LLM and therefore is limited by the LLM used.
       Lolive, Damien  and
       Delhay, Arnaud  and
       Barbot, Nelly",
-    editor = "Rambow, Owen  and
-      Wanner, Leo  and
-      Apidianaki, Marianna  and
-      Al-Khalifa, Hend  and
-      Eugenio, Barbara Di  and
-      Schockaert, Steven",
     booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
-    month = jan,
     year = "2025",
-    address = "Abu Dhabi, UAE",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2025.coling-main.538/",
-    pages = "8057--8087",
-    abstract = "Evaluating automatic paraphrase production systems is a difficult task as it involves, among other things, assessing the semantic proximity between two sentences. Usual measures are based on lexical distances, or at least on semantic embedding alignments. The rise of Large Language Models (LLM) has provided tools to model relationships within a text thanks to the attention mechanism. In this article, we introduce ParaPLUIE, a new measure based on a log likelihood ratio from an LLM, to assess the quality of a potential paraphrase. This measure is compared with usual measures on two known by the NLP community datasets prior to this study. Three new small datasets have been built to allow metrics to be compared in different scenario and to avoid data contamination bias. According to evaluations, the proposed measure is better for sorting pairs of sentences by semantic proximity. In particular, it is much more independent to lexical distance and provides an interpretable classification threshold between paraphrases and non-paraphrases."
+    url = "https://aclanthology.org/2025.coling-main.538/"
 }
 ```