2 papers
cs.LG2025
The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models
Matthew Riemer, Erik Miehling, Miao Liu +2
Although parameter-efficient fine-tuning methods, such as LoRA, only modify a small subset of parameters, they can have a significant impact on the model. Our instruction-tuning ex…
cs.AI2025
Position: Theory of Mind Benchmarks are Broken for Large Language Models
Matthew Riemer, Zahra Ashktorab, Djallel Bouneffouf +4
Our paper argues that the majority of theory of mind benchmarks are broken because of their inability to directly test how large language models (LLMs) adapt to new partners. This…