Update evaluation/demo_humaneval.md

evaluation/demo_humaneval.md

@@ -40,7 +40,7 @@ Instead of 200 candidate solutions, we will only generate 20 samples for illustr
 
 **Remark**:
 
-Regarding the temperature parameter, in [CodeGen](https://github.com/salesforce/CodeGen) paper, the authors observed that the best performing temperature increases as the number of samples permitted k increases. When a model is only allowed a few samples to pass unit tests, it is beneficial to use the learned distribution, through a low temperature, to select candidates that are likely to pass. But when a model is allowed for more chances with a high k, using a higher sampling temperature to tilt the learned model distribution lets it explore diverse samples and thus have a greater chance of synthesizing a correct program. 
+Regarding the temperature parameter, in [Codex](https://arxiv.org/pdf/2107.03374.pdf) paper, the authors observed that the best performing temperature increases as the number of samples permitted k increases. Similar behavior was also observed in [CodeGen](https://arxiv.org/pdf/2203.13474.pdf). When a model is only allowed a few samples to pass unit tests, it is beneficial to use the learned distribution, through a low temperature, to select candidates that are likely to pass. But when a model is allowed for more chances with a high k, using a higher sampling temperature to tilt the learned model distribution lets it explore diverse samples and thus have a greater chance of synthesizing a correct program.
 
 
 For our experiment, we compute pass@1, pass@10 and pass@20, each corresponding to unit test pass rate when selecting respectively 1, 10 and 20 samples from the candidate solutions.