most citedMeasure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests

1 citations · 1 across the 13 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI2026

Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models

Benjamin Maltbie, Shivam Raval

Large language models exhibit sycophantic tendencies, but whether this behavior varies systematically with perceived user demographics is underexplored. Inspired by intersectionali…

cs.AI2026

Curveball Steering: The Right Direction To Steer Isn't Always Linear

Shivam Raval, Hae Jin Song, Linlin Wu +4

Activation steering is a widely used approach for controlling large language model (LLM) behavior by intervening on internal representations. Existing methods largely rely on the L…

cs.AI2026

Narrow Fine-Tuning Erodes Safety Alignment in Vision-Language Agents

Idhant Gulati, Shivam Raval

Lifelong multimodal agents must continuously adapt to new tasks through post-training, but this creates a fundamental tension between acquiring capabilities and preserving safety a…

cs.AI20251 cited

Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests

Alexandra Yost, Shreyans Jain, Shivam Raval +6

Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation. We prop…

cs.AI2025

Linear probes rely on textual evidence: Results from leakage mitigation studies in language models

Gerard Boxo, Aman Neelappa, Shivam Raval

White-box monitors are a popular technique for detecting potentially harmful behaviours in language models. While they perform well in general, their effectiveness in detecting tex…