A comprehensive survey of artificial intelligence techniques for talent analytics
In today's competitive and fast-evolving business environment, it is a critical time for
organizations to rethink how to make talent-related decisions in a quantitative manner …
organizations to rethink how to make talent-related decisions in a quantitative manner …
The wmdp benchmark: Measuring and reducing malicious use with unlearning
The White House Executive Order on Artificial Intelligence highlights the risks of large
language models (LLMs) empowering malicious actors in developing biological, cyber, and …
language models (LLMs) empowering malicious actors in developing biological, cyber, and …
Llm evaluators recognize and favor their own generations
Self-evaluation using large language models (LLMs) has proven valuable not only in
benchmarking but also methods like reward modeling, constitutional AI, and self-refinement …
benchmarking but also methods like reward modeling, constitutional AI, and self-refinement …
Recommendation with generative models
Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …
learning and sampling from their statistical distributions. In recent years, these models have …
Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
As artificial intelligence systems grow more powerful, there has been increasing interest in"
AI safety" research to address emerging and future risks. However, the field of AI safety …
AI safety" research to address emerging and future risks. However, the field of AI safety …
Quiet-star: Language models can teach themselves to think before speaking
E Zelikman, G Harik, Y Shao, V Jayasiri… - arXiv preprint arXiv …, 2024 - arxiv.org
When writing and talking, people sometimes pause to think. Although reasoning-focused
works have often framed reasoning as a method of answering questions or completing …
works have often framed reasoning as a method of answering questions or completing …
Beyond static AI evaluations: advancing human interaction evaluations for LLM harms and risks
Model evaluations are central to understanding the safety, risks, and societal impacts of AI
systems. While most real-world AI applications involve human-AI interaction, most current …
systems. While most real-world AI applications involve human-AI interaction, most current …
Bells: A framework towards future proof benchmarks for the evaluation of llm safeguards
Input-output safeguards are used to detect anomalies in the traces produced by Large
Language Models (LLMs) systems. These detectors are at the core of diverse safety-critical …
Language Models (LLMs) systems. These detectors are at the core of diverse safety-critical …
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
As AI systems enter into a growing number of societal domains, these systems increasingly
shape and are shaped by user preferences, opinions, and behaviors. However, the design …
shape and are shaped by user preferences, opinions, and behaviors. However, the design …
Human expertise in algorithmic prediction
We introduce a novel framework for incorporating human expertise into algorithmic
predictions. Our approach leverages human judgment to distinguish inputs which are …
predictions. Our approach leverages human judgment to distinguish inputs which are …