The increasing use of foundation models highlights the urgent need to address and eliminate implicit biases present in them that arise during pretraining.In this paper, we introduce PEFTDebias, a novel approach that employs parameter-efficient fine-tuning (PEFT) to mitigate the biases within foundation models.PEFTDebias consists of two main phases: an upstream phase for acquiring debiasing parameters along a specific bias axis, and a downstream phase where these parameters are incorporated into the model and frozen during the fine-tuning process.By evaluating on four datasets across two bias axes namely gender and race, we find that downstream biases can be effectively reduced with PEFTs.In addition, we show that these parameters possess axis-specific debiasing characteristics, enabling their effective transferability in mitigating biases in various downstream tasks.To ensure reproducibility, we release the code to do our experiments 1 .

Since the rise of neural natural-language-tocode models (NL→Code) that can generate long expressions and statements rather than a single next-token, one of the major problems has been reliably evaluating their generated output.In this paper, we propose CodeBERTScore: an evaluation metric for code generation, which builds on BERTScore (Zhang et al., 2020).Instead of encoding only the generated tokens as in BERTScore, CodeBERTScore also encodes the natural language input preceding the generated code, thus modeling the consistency between the generated code and its given natural language context as well.We perform an extensive evaluation of CodeBERTScore across four programming languages.We find that Code-BERTScore achieves a higher correlation with human preference and with functional correctness than all existing metrics.That is, generated code that receives a higher score by Code-BERTScore is more likely to be preferred by humans, as well as to function correctly when executed.We release five language-specific pretrained models to use with our publicly available code.Our language-specific models have been downloaded more than 1,000,000 times from the Huggingface Hub. 1

Code-switched (CS) data is ubiquitous in today's globalized world, but the dearth of annotated datasets in code-switching poses a significant challenge for learning diverse tasks across different language pairs.Parameter-efficient prompt-tuning approaches conditioned on frozen language models have shown promise for transfer learning in limited-resource setups.In this paper, we propose a novel instancebased prompt composition technique, PRO-CS, for CS tasks that combine language and task knowledge.We compare our approach with prompt-tuning and fine-tuning for codeswitched tasks on 10 datasets across 4 language pairs.Our model outperforms the prompttuning approach by significant margins across all datasets and outperforms or remains at par with fine-tuning by using just 0.18% of total parameters.We also achieve competitive results when compared with the fine-tuned model in the low-resource cross-lingual and crosstask setting, indicating the effectiveness of our approach to incorporate new code-switched tasks.

Online Social Networks (OSNs) have emerged as a global media for forming and shaping opinions on a broad spectrum of topics like politics, e-commerce, sports, etc. So, research on understanding and predicting opinion dynamics in OSNs, especially using a tractable linear model, has abound in literature. However, these linear models are too simple to uncover the actual complex dynamics of opinion flow in social networks. In this paper, we propose SLANT+, a novel nonlinear generative model for opinion dynamics, by extending our earlier linear opinion model SLANT [7]. To design this model, we rely on a network-guided recurrent neural network architecture which learns a proper temporal representation of the messages as well as the underlying network. Furthermore, we probe various signals from the real life datasets and offer a conceptually interpretable nonlinear function that not only provides concrete clues of the opinion exchange process, but also captures the coupled dynamics of message timings and opinion flow. As a result, with five real-life datasets crawled from Twitter, our proposal gives significant accuracy boost over six state-of-the-art baselines.