The key feature of circuits is that they enable one to precisely characterize tractability conditions (structural properties of the circuit) under which a given inference query can be computed exactly and efficiently. One can then enforce these circuit properties when compiling or learning a model to enable tractable inference.

Experiments show that training on the high-quality data selected by our method can often outperform other data selection methods for collaborative fine-tuning of LLMs, across diverse private domain datasets, in medical, multilingual and financial settings.

Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast. Our initial run evaluates 22 VLMs on 21 existing datasets to provide a holistic snapshot of the models. We uncover new key findings, such as the fact that efficiency-focused models (e.g., Claude 3 Haiku or Gemini 1.5 Flash) perform significantly

TS also demonstrates strong few-shot and prompt capabilities when applied to new domains and tasks.

In the standard reinforcement learning (RL) setting, the primary goal is to obtain a policy that maximizes a cumulative scalar reward [Sutton and Barto, 2018].

California's landmark frontier AI law to bring transparency Late last month, California became the first state in the United States to pass a law to regulate cutting-edge AI technologies. Now experts are divided over its impact. They agree that the law, the Transparency in Frontier Artificial Intelligence Act, is a modest step forward, but it is still far from actual regulation. It mandates reporting of incidents such as large-scale cyber-attacks, deaths of 50 or more people, large monetary losses and other safety-related events caused by AI models. It also puts in place whistleblower protections.

The complexity of automating bias evaluation in textual content poses significant challenges. Traditional approaches in social sciences, such as content analysis, often rely on manual word counts from static lists [Gaucher et al., 2011], which may miss the subtleties and unlisted language cues that advanced NLP technologies can detect.