17 citations · 73 across the 25 of their papers we have counts for
10 papers · 1 filter
OpenThoughts: Data Recipes for Reasoning Models
Etash Guha, Ryan Marten, Sedrick Keh +47
Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoni…
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Alon Albalak, Duy Phung, Nathan Lile +8
Increasing interest in reasoning models has led math to become a prominent testing ground for algorithmic and methodological improvements. However, existing open math datasets eith…
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
Alex Havrilla, Andrew Dai, Laura O'Mahony +17
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct compariso…
Generative Reward Models
Dakota Mahan, Duy Van Phung, Rafael Rafailov +6
Reinforcement Learning from Human Feedback (RLHF) has greatly improved the performance of modern Large Language Models (LLMs). The RLHF process is resource-intensive and technicall…
A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems
Charles Dickens, Connor Pryor, Changyu Gao +5
The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, a unif…
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Shayne Longpre, Stella Biderman, Alon Albalak +20
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foun…
DataComp-LM: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis +56
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardize…
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
Alon Albalak, Colin Raffel, William Yang Wang
Few-shot learning is valuable in many real-world applications, but learning a generalizable model without overfitting to the few labeled datapoints is challenging. In this work, we…
Emotion Recognition in Conversation using Probabilistic Soft Logic
Eriq Augustine, Pegah Jandaghi, Alon Albalak +4
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NL…
NeuPSL: Neural Probabilistic Soft Logic
Connor Pryor, Charles Dickens, Eriq Augustine +3
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level per…