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Found 21 papers, 7 papers with code
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
Understanding the Effects of Iterative Prompting on Truthfulness
The development of Large Language Models (LLMs) has notably transformed numerous sectors, offering impressive text generation capabilities.
Black-Box Access is Insufficient for Rigorous AI Audits
The effectiveness of an audit, however, depends on the degree of system access granted to auditors.
On the Intersection of Self-Correction and Trust in Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex cognitive tasks.
Are Large Language Models Post Hoc Explainers?
To this end, several approaches have been proposed in recent literature to explain the behavior of complex predictive models in a post hoc fashion.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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On the Trade-offs between Adversarial Robustness and Actionable Explanations
As machine learning models are increasingly being employed in various high-stakes settings, it becomes important to ensure that predictions of these models are not only adversarially robust, but also readily explainable to relevant stakeholders.
Post Hoc Explanations of Language Models Can Improve Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks.
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications.
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
In real-world evaluations with humans, 73% of healthcare workers (e. g., doctors and nurses) agreed they would use TalkToModel over baseline point-and-click systems for explainability in a disease prediction task, and 85% of ML professionals agreed TalkToModel was easier to use for computing explanations.
OpenXAI: Towards a Transparent Evaluation of Model Explanations
OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets.
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal
Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings.
Measuring Fairness of Text Classifiers via Prediction Sensitivity
With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions.
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are stable, e. g., robust to infinitesimal perturbations to an input.
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
To this end, we first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding.
Towards Realistic Single-Task Continuous Learning Research for NER
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training.
Grounding Complex Navigational Instructions Using Scene Graphs
Training a reinforcement learning agent to carry out natural language instructions is limited by the available supervision, i. e. knowing when the instruction has been carried out.
ADePT: Auto-encoder based Differentially Private Text Transformation
We prove the theoretical privacy guarantee of our algorithm and assess its privacy leakage under Membership Inference Attacks(MIA) (Shokri et al., 2017) on models trained with transformed data.
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23, 679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology.
Towards classification parity across cohorts
In this research work, we aim to achieve classification parity across explicit as well as implicit sensitive features.
FineText: Text Classification via Attention-based Language Model Fine-tuning
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge, which usually cannot be satisfied by the customers.
