A Trip Towards Fairness: Bias and De-Biasing in Large Language Models

An outbreak in the popularity of transformer-based Language Models (such as GPT (Brown et al., 2020) and PaLM (Chowdhery et al., 2022)) has opened the doors to new Machine Learning applications. In particular, in Natural Language Processing and how pre-training from large text, corpora is essential in achieving remarkable results in downstream tasks. However, these Language Models seem to have inherent biases toward certain demographics reflected in their training data. While research has attempted to mitigate this problem, existing methods either fail to remove bias altogether, degrade performance, or are expensive. This paper examines the bias produced by promising Language Models when varying parameters and pre-training data. Finally, we propose a de-biasing technique that produces robust de-bias models that maintain performance on downstream tasks

The Dark Side of the Language: Pre-trained Transformers in the DarkNet

Pre-trained Transformers are challenging human performances in many natural language processing tasks. The gigantic datasets used for pre-training seem to be the key for their success on existing tasks. In this paper, we explore how a range of pre-trained natural language understanding models perform on truly novel and unexplored data, provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks largely outperform pre-trained Transformers. This seems to suggest that pre-trained Transformers have serious difficulties in adapting to radically novel texts

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Genotype and allele frequencies of evaluated SNPs.

<p>Genotype and allele frequencies of evaluated SNPs.</p

Patients’ characteristics.

<p>Patients’ characteristics.</p

Progression free survival (PFS, expressed in months) according to selected VEGF-A, VEGFR-2 and VEGFR-3 polymorphisms.

<p>a) VEGF-A polymorphisms. b) VEGFR-2 polymorphisms. c) VEGFR-3 polymorphisms. d) Poor group (patients presenting all 3 polymorphisms) vs not poor group (patients presenting 1 or 2 polymorphisms). e) High-risk group (patients presenting all 3 polymorphisms) vs intermediate-risk group (patients presenting 2 polymorphisms) vs low-risk group (patients presenting at most 1 polymorphisms).</p

Surgical and medical history of the enrolled patients.

<p>Surgical and medical history of the enrolled patients.</p