activity
20202026
most citedMisinformation Has High Perplexity

25 citations · 73 across the 21 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

Thomson: Continual Learning of Frontier Models for SovereignAI

Shengzhuang Chen, Jerrod Parker, Yejin Bang +23

The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetr…

cs.AI2026

ContractScrub: A benchmark for final review of legal contracts

Yejin Bang, Kirsty Fielding, Brandan Oliver +3

Legal work, with its heavy reliance on processing large amounts of text, is often considered one of the domains most exposed to the use of LLMs. Contract ``scrubbing,'' the final r…

cs.AI2026

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models

John Scoville, Shengzhuang Chen, Yejin Bang +2

Recent meta-reasoning frameworks improve LLM reasoning by wrapping chain-of-thought generation in an iterative control loop, allowing more effective backtracking, termination of re…

cs.AI20251 cited

Planning with Reasoning using Vision Language World Model

Delong Chen, Theo Moutakanni, Willy Chung +4

Effective planning requires strong world models, but high-level world models that can understand and reason about actions with semantic and temporal abstraction remain largely unde…

cs.AI20212 cited

Dynamically Addressing Unseen Rumor via Continual Learning

Nayeon Lee, Andrea Madotto, Yejin Bang +1

Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address thi…