2 citations · 2 across the 6 of their papers we have counts for
6 papers
Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model
Simiao Ren, Kidus Zewde, Xingyu Shen +6
Screening a phone call for fraud needs a trustworthy probability after every caller turn, in milliseconds. Jev-style typed decisions promise exactly that: declared options go in, o…
Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate
Ethan Traister, Ankit Raj, Jiaqi Gan +7
Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of aud…
CallScreenBench: Benchmarking Small Language Models as Phone Secretaries
Jiaqi Gan, Haoyuan Tang, Jamey Z. Liang +6
Language models small enough to run on a handset, quantized to a few bits, are increasingly capable of acting on their user's behalf -- which makes on-device task automation newly…
A Synthetic Eye Movement Dataset for Script Reading Detection: Real Trajectory Replay on a 3D Simulator
Kidus Zewde, Yuchen Zhou, Dennis Ng +6
Large vision-language models have achieved remarkable capabilities by training on massive internet-scale data, yet a fundamental asymmetry persists: while LLMs can leverage self-su…
Chinese Language Is Not More Efficient Than English in Vibe Coding: A Preliminary Study on Token Cost and Problem-Solving Rate
Simiao Ren, Xingyu Shen, Yuchen Zhou +3
A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, potentially reducing costs by…
Divergences between Language Models and Human Brains
Yuchen Zhou, Emmy Liu, Graham Neubig +2
Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the in…