4 citations · 7 across the 19 of their papers we have counts for
8 papers · 1 filter
Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining
Lukas Thede, Shengzhuang Chen, Stefan Winzeck +3
Continued pretraining enables language models to adapt to new domains and knowledge, but often at the cost of forgetting previously acquired capabilities. Replay can mitigate this…
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…
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…
PreAct-Bench: Benchmarking Predictive Monitoring in LLMs
Hainiu Xu, Italo Luis da Silva, Jiangnan Ye +7
Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objective. While existing safety rese…
To Whom Do Language Models Align? Measuring Principal Hierarchies Under High-Stakes Competing Demands
Fangyi Yu, Nabeel Seedat, Jonathan Richard Schwarz +1
Language models deployed in high-stakes professional settings face conflicting demands from users, institutional authorities, and professional norms. How models act when these dema…
CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training
Lukas Thede, Stefan Winzeck, Zeynep Akata +1
Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves performance, and enables domain adaptation. Unfortunately, post-training is known…
Aligning Language Model Benchmarks with Pairwise Preferences
Marco Gutierrez, Xinyi Leng, Hannah Cyberey +3
Language model benchmarks are pervasive and computationally-efficient proxies for real-world downstream performance. However, many recent works find that benchmarks often fail to p…
Knowledge Graph-Assisted LLM Post-Training for Enhanced Legal Reasoning
Dezhao Song, Guglielmo Bonifazi, Frank Schilder +1
LLM post-training has primarily relied on large text corpora and human feedback, without capturing the structure of domain knowledge. This has caused models to struggle dealing wit…