activity
20242026
most citedWhy Language Models Hallucinate

34 citations · 36 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight

Can Jin, Jiakang Li, Rui Wu +2

As large language models become stronger, weak supervisors may fail to provide reliable labels, preferences, or final judgments for complex outputs, limiting both weak-to-strong ge…

cs.CL2025★ 34 cited

Why Language Models Hallucinate

Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala +1

Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such…

cs.AI2025

A Taxonomy of Transcendence

Natalie Abreu, Edwin Zhang, Eran Malach +1

Although language models are trained to mimic humans, the resulting systems display capabilities beyond the scope of any one person. To understand this phenomenon, we use a control…

cs.LG2025★ 1 cited

Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding

Donghwa Shin, Edwin Zhang

Transformers have recently gained popularity in time series forecasting due to their ability to capture long-term dependencies. However, many existing models focus only on capturin…

cs.LG2024★ 1 cited

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning

Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6

Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…