18 research outputs found
Conceptor-Aided Debiasing of Contextualized Embeddings
Pre-trained language models reflect the inherent social biases of their
training corpus. Many methods have been proposed to mitigate this issue, but
they often fail to debias or they sacrifice model accuracy. We use
conceptors--a soft projection method--to identify and remove the bias subspace
in contextual embeddings in BERT and GPT. We propose two methods of applying
conceptors (1) bias subspace projection by post-processing; and (2) a new
architecture, conceptor-intervened BERT (CI-BERT), which explicitly
incorporates the conceptor projection into all layers during training. We find
that conceptor post-processing achieves state-of-the-art debiasing results
while maintaining or improving BERT's performance on the GLUE benchmark.
Although CI-BERT's training takes all layers' bias into account and can
outperform its post-processing counterpart in bias mitigation, CI-BERT reduces
the language model accuracy. We also show the importance of carefully
constructing the bias subspace. The best results are obtained by removing
outliers from the list of biased words, intersecting them (using the conceptor
AND operation), and computing their embeddings using the sentences from a
cleaner corpus.Comment: 23 page
Latent space exploration and functionalization of a gated working memory model using conceptors
International audienceIntroduction. Working memory is the ability to maintain and manipulate information. We introduce a method based on conceptors that allows us to manipulate information stored in the dynamics (latent space) of a gated working memory model. Methods. This latter model is based on a reservoir: a random recurrent network with trainable readouts. It is trained to hold a value in memory given an input stream when a gate signal is on and to maintain this information when the gate is off. The memorized information results in complex dynamics inside the reservoir that can be faithfully captured by a conceptor.Results. Such conceptors allow us to explicitly manipulate this information in order to perform various, but not arbitrary, operations. In this work, we show how working memory can be stabilized or discretized using such conceptors, how such conceptors can be linearly combined to form new memories, and how these conceptors can be extended to a functional role. Conclusion. These preliminary results suggest that conceptors can be used to manipulate the latent space of the working memory even though several results we introduce are not as intuitive as one would expect
Continual Learning of Natural Language Processing Tasks: A Survey
Continual learning (CL) is an emerging learning paradigm that aims to emulate
the human capability of learning and accumulating knowledge continually without
forgetting the previously learned knowledge and also transferring the knowledge
to new tasks to learn them better. This survey presents a comprehensive review
of the recent progress of CL in the NLP field. It covers (1) all CL settings
with a taxonomy of existing techniques. Besides dealing with forgetting, it
also focuses on (2) knowledge transfer, which is of particular importance to
NLP. Both (1) and (2) are not mentioned in the existing survey. Finally, a list
of future directions is also discussed
Reservoir SMILES: Towards SensoriMotor Interaction of Language and Embodiment of Symbols with Reservoir Architectures
Language involves several hierarchical levels of abstraction. Most models focus on a particular level of abstraction making them unable to model bottom-up and top-down processes. Moreover, we do not know how the brain grounds symbols to perceptions and how these symbols emerge throughout development. Experimental evidence suggests that perception and action shape one-another (e.g. motor areas activated during speech perception) but the precise mechanisms involved in this action-perception shaping at various levels of abstraction are still largely unknown. My previous and current work include the modelling of language comprehension, language acquisition with a robotic perspective, sensorimotor models and extended models of Reservoir Computing to model working memory and hierarchical processing. I propose to create a new generation of neural-based computational models of language processing and production; to use biologically plausible learning mechanisms relying on recurrent neural networks; create novel sensorimotor mechanisms to account for action-perception shaping; build hierarchical models from sensorimotor to sentence level; embody such models in robots
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea
Ministry of Science, ICT & Future Planning, Republic of Korea
Ministry of Science & ICT (MSIT), Republic of Korea
2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program
IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068
Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning
Contains fulltext :
228326pre.pdf (preprint version ) (Open Access)
Contains fulltext :
228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202