95 citations · 417 across the 43 of their papers we have counts for
7 papers · 1 filter
Not All LLM Reasoners Are Created Equal
Arian Hosseini, Alessandro Sordoni, Daniel Toyama +2
We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To this end, we evaluate their performance on pairs of existing math word problems together so t…
VinePPO: Refining Credit Assignment in RL Training of LLMs
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance +4
Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit…
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Prateek Yadav, Colin Raffel, Mohammed Muqeeth +6
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods…
Improving Context-Aware Preference Modeling for Language Models
Silviu Pitis, Ziang Xiao, Nicolas Le Roux +1
While finetuning language models from pairwise preferences has proven remarkably effective, the underspecified nature of natural language presents critical challenges. Direct prefe…
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti +5
The growing number of parameter-efficient adaptations of a base large language model (LLM) calls for studying whether we can reuse such trained adapters to improve performance for…
Efficient Adversarial Training in LLMs with Continuous Attacks
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann +2
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most p…