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Found 28 papers, 12 papers with code
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories.
Humanity's Last Exam
However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities.
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Humanity's Last Exam
on Humanity's Last Exam
Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL
Large language models (LLMs) trained with Reinforcement Learning from Human Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying reward functions and decision-making processes remain opaque.
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
The accuracy is significantly improved with our proposed multi-step retrieval pipeline, achieving an accuracy of 0. 66 (>50% improvement).
Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)
By delineating prominent attack motifs and shedding light on various entry points, this paper provides a framework for improving the security and robustness of LLM-based systems.
More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness
The trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethically aligned, and it has become a crucial consideration alongside their cognitive performance.
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.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point.
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
External audits of AI systems are increasingly recognized as a key mechanism for AI governance.
On the Intersection of Self-Correction and Trust in Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex cognitive tasks.
In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
Despite their effectiveness in enhancing the performance of LLMs on diverse language and tabular tasks, these methods have not been thoroughly explored for their potential to generate post hoc explanations.
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
In addition, we carry out an online user study with data scientists to understand how they resolve the aforementioned disagreements.
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.
