7 citations · 31 across the 20 of their papers we have counts for
8 papers · 1 filter
Fluid Language Model Benchmarking
Valentin Hofmann, David Heineman, Ian Magnusson +7
Language model (LM) benchmarking faces several challenges: comprehensive evaluations are costly, benchmarks often fail to measure the intended capabilities, and evaluation quality…
FlexOlmo: Open Language Models for Flexible Data Use
Weijia Shi, Akshita Bhagia, Kevin Farhat +20
We introduce FlexOlmo, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained…
Frustratingly Simple Retrieval Improves Challenging, Reasoning-Intensive Benchmarks
Xinxi Lyu, Michael Duan, Rulin Shao +2
Retrieval-augmented Generation (RAG) has primarily been studied in limited settings, such as factoid question answering; more challenging, reasoning-intensive benchmarks have seen…
ReasonIR: Training Retrievers for Reasoning Tasks
Rulin Shao, Rui Qiao, Varsha Kishore +8
We present ReasonIR-8B, the first retriever specifically trained for general reasoning tasks. Existing retrievers have shown limited gains on reasoning tasks, in part because exist…
Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions
Rui Qiao, Zhaoxuan Wu, Jingtan Wang +2
Machine learning models often have uneven performance among subpopulations (a.k.a., groups) in the data distributions. This poses a significant challenge for the models to generali…
EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees
Zhiyuan Zeng, Yizhong Wang, Hannaneh Hajishirzi +1
An ideal model evaluation should achieve two goals: identifying where the model fails and providing actionable improvement guidance. Toward these goals for language model (LM) eval…
S4S: Solving for a Diffusion Model Solver
Eric Frankel, Sitan Chen, Jerry Li +3
Diffusion models (DMs) create samples from a data distribution by starting from random noise and iteratively solving a reverse-time ordinary differential equation (ODE). Because ea…
ICONS: Influence Consensus for Vision-Language Data Selection
Xindi Wu, Mengzhou Xia, Rulin Shao +3
Training vision-language models via instruction tuning relies on large data mixtures spanning diverse tasks and domains, yet these mixtures frequently include redundant information…