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Add references #6

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Expand Up @@ -50,6 +50,7 @@ You can browse papers by Machine Learning task category, and use hashtags like `
## Natural Language Processing

* [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList](http://homes.cs.washington.edu/~marcotcr/acl20_checklist.pdf) (Ribeiro et al., 2020) `#Robustness`
* [Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization](https://arxiv.org/abs/2305.12782) (Chen et al. 2023)`#Robustness`
* [Pipelines for Social Bias Testing of Large Language Models](https://openreview.net/pdf/8be28761ea130113e3be7747870c434f53e9b309.pdf) (Nozza et al., 2022) `#Bias` `#Ethics`
* [Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://arxiv.org/abs/1602.04938) (Ribeiro et al., 2016) `#Explainability`
* [A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874) (Lundberg et al., 2017) `#Explainability`
Expand All @@ -65,6 +66,7 @@ You can browse papers by Machine Learning task category, and use hashtags like `

### Large Language Models

* [Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators](https://arxiv.org/abs/2310.07289) (Chen et al., 2023) `#reliability`
* [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) (Liang et al., 2022) `#General`
* [Learning to summarize from human feedback](https://proceedings.neurips.cc/paper/2020/file/1f89885d556929e98d3ef9b86448f951-Paper.pdf) (Stiennon et al., 2020) `#HumanFeedback`
* [Identifying and Reducing Gender Bias in Word-Level Language Models](https://arxiv.org/abs/1904.03035) (Bordia and Bowman, 2019) `#Bias`
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