most citedStatutory Construction and Interpretation for Artificial Intelligence

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

astro-ph.IM20251 cited

AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy

Jinghang Shi, Xiaoyu Tang, Yang Huang +4

Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…

cs.CL20251 cited

Statutory Construction and Interpretation for Artificial Intelligence

Luxi He, Nimra Nadeem, Michel Liao +4

AI systems are increasingly governed by natural language principles, yet a key challenge arising from reliance on language remains underexplored: interpretive ambiguity. As in lega…

cs.SD2025

The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Privilege

Luxi He, Xiangyu Qi, Michel Liao +4

The latest Audio Language Models (Audio LMs) process speech directly instead of relying on a separate transcription step. This shift preserves detailed information, such as intonat…

cs.CL2025

Metadata Conditioning Accelerates Language Model Pre-training

Tianyu Gao, Alexander Wettig, Luxi He +3

The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently lear…

cs.CR2024

On Evaluating the Durability of Safeguards for Open-Weight LLMs

Xiangyu Qi, Boyi Wei, Nicholas Carlini +7

Stakeholders -- from model developers to policymakers -- seek to minimize the dual-use risks of large language models (LLMs). An open challenge to this goal is whether technical sa…