1 citations · 1 across the 4 of their papers we have counts for
5 papers
Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs
Ziling Cheng, Meng Cao, Marc-Antoine Rondeau +1
The widespread success of large language models (LLMs) on NLP benchmarks has been accompanied by concerns that LLMs function primarily as stochastic parrots that reproduce texts si…
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Yoshua Bengio, Michael Cohen, Damiano Fornasiere +10
The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue goals across almost all tasks that humans…
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling
Yu Bai, Xiyuan Zou, Heyan Huang +4
Long sequence modeling has gained broad interest as large language models (LLMs) continue to advance. Recent research has identified that a large portion of hidden states within th…
Identifying and Analyzing Performance-Critical Tokens in Large Language Models
Yu Bai, Heyan Huang, Cesare Spinoso-Di Piano +4
In-context learning (ICL) has emerged as an effective solution for few-shot learning with large language models (LLMs). However, how LLMs leverage demonstrations to specify a task…
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté +7
Playing text-based games requires skills in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challe…