diff --git a/metrics/mauve/README.md b/metrics/mauve/README.md
index 3ba39d8fa..06a9c53f9 100644
--- a/metrics/mauve/README.md
+++ b/metrics/mauve/README.md
@@ -25,7 +25,7 @@ MAUVE is a measure of the gap between neural text and human text. It is computed
 
 This metric is a wrapper around the [official implementation](https://github.com/krishnap25/mauve) of MAUVE.
 
-For more details, consult the [MAUVE paper](https://arxiv.org/abs/2102.01454).
+For more details, consult the MAUVE papers [[NeurIPS '21](https://arxiv.org/abs/2102.01454), [JMLR '23](https://arxiv.org/pdf/2212.14578)].
 
 ## How to use
 
@@ -69,12 +69,16 @@ It also has several optional arguments:
 
 ## Output values
 
-This metric outputs a dictionary with 5 key-value pairs:
+This metric outputs a dictionary with the following key-value pairs:
 
 `mauve`: MAUVE score, which ranges between 0 and 1. **Larger** values indicate that P and Q are closer.
 
 `frontier_integral`: Frontier Integral, which ranges between 0 and 1. **Smaller** values indicate that P and Q are closer.
 
+'mauve_star`: MAUVE score computed with Krichevsky-Trofimov smoothing, as advocated in [this paper](https://arxiv.org/pdf/2212.14578).
+
+'frontier_integral_star`: Frontier Interal score computed with Krichevsky-Trofimov smoothing, as advocated in [this paper](https://arxiv.org/pdf/2212.14578).
+
 `divergence_curve`: a numpy.ndarray of shape (m, 2); plot it with `matplotlib` to view the divergence curve.
 
 `p_hist`: a discrete distribution, which is a quantized version of the text distribution `p_text`.
@@ -131,11 +135,25 @@ See the [official implementation](https://github.com/krishnap25/mauve#best-pract
 ## Citation
 
 ```bibtex
+@article{pillutla-etal:mauve:jmlr2023,
+  title={{MAUVE Scores for Generative Models: Theory and Practice}},
+  author={Pillutla, Krishna and Liu, Lang and Thickstun, John and Welleck, Sean and Swayamdipta, Swabha and Zellers, Rowan and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
+  journal={JMLR},
+  year={2023}
+}
+
 @inproceedings{pillutla-etal:mauve:neurips2021,
-  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
+  title={{MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers}},
   author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
   booktitle = {NeurIPS},
-  year  	= {2021}
+  year      = {2021}
+}
+
+@inproceedings{liu-etal:mauve-theory:neurips2021,
+  title={{Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals}},
+  author={Liu, Lang and Pillutla, Krishna and Welleck, Sean and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
+  booktitle={NeurIPS},
+  year={2021}
 }
 ```
 
diff --git a/metrics/mauve/mauve.py b/metrics/mauve/mauve.py
index fdacaa477..4f7b17a94 100644
--- a/metrics/mauve/mauve.py
+++ b/metrics/mauve/mauve.py
@@ -26,6 +26,13 @@
 
 
 _CITATION = """\
+        @article{pillutla-etal:mauve:jmlr2023,
+  title={{MAUVE Scores for Generative Models: Theory and Practice}},
+  author={Pillutla, Krishna and Liu, Lang and Thickstun, John and Welleck, Sean and Swayamdipta, Swabha and Zellers, Rowan and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
+  journal={JMLR},
+  year={2023}
+}
+
 @inproceedings{pillutla-etal:mauve:neurips2021,
   title={{MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers}},
   author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
@@ -33,11 +40,11 @@
   year      = {2021}
 }
 
-@article{pillutla-etal:mauve:arxiv2022,
-  title={{MAUVE Scores for Generative Models: Theory and Practice}},
-  author={Pillutla, Krishna and Liu, Lang and Thickstun, John and Welleck, Sean and Swayamdipta, Swabha and Zellers, Rowan and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
-  journal={arXiv Preprint},
-  year={2022}
+@inproceedings{liu-etal:mauve-theory:neurips2021,
+  title={{Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals}},
+  author={Liu, Lang and Pillutla, Krishna and Welleck, Sean and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
+  booktitle={NeurIPS},
+  year={2021}
 }
 """
 
@@ -75,6 +82,8 @@
 Returns:
     mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
     frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
+    mauve_star: MAUVE score computed with Krichevsky-Trofimov smoothing, as advocated in [this paper](https://arxiv.org/pdf/2212.14578),
+    frontier_integral_star: Frontier Interal score computed with Krichevsky-Trofimov smoothing, as advocated in [this paper](https://arxiv.org/pdf/2212.14578),
     divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
     p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
     q_hist: same as above, but with q_text.