16 citations · 63 across the 18 of their papers we have counts for
15 papers · 1 filter
Steerable Cultural Preference Optimization of Reward Models
Minsik Oh, Advit Deepak, Sophie Wu +2
It is essential for large language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community. However, research on LL…
Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
Dat Tran, Douwe Kiela
Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation. When computation is normalized,…
Document Optimization for Black-Box Retrieval via Reinforcement Learning
Omri Uzan, Ron Polonsky, Douwe Kiela +1
Document expansion is a classical technique for improving retrieval quality, and is attractive since it shifts computation offline, avoiding additional query-time processing. Howev…
LMUnit: Fine-grained Evaluation with Natural Language Unit Tests
Jon Saad-Falcon, Rajan Vivek, William Berrios +6
As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge -- human evaluation is costly and noisy, while automated metrics…
OLMoE: Open Mixture-of-Experts Language Models
Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld +21
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input to…
Generative Representational Instruction Tuning
Niklas Muennighoff, Hongjin Su, Liang Wang +5
All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational in…