51 citations · 266 across the 32 of their papers we have counts for
8 papers · 1 filter
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…
Nearest Neighbor Normalization Improves Multimodal Retrieval
Neil Chowdhury, Franklin Wang, Sumedh Shenoy +3
Multimodal models leverage large-scale pre-training to achieve strong but still imperfect performance on tasks such as image captioning, visual question answering, and cross-modal…
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
Karel D'Oosterlinck, Winnie Xu, Chris Develder +5
Large Language Models (LLMs) are often aligned using contrastive alignment objectives and preference pair datasets. The interaction between model, paired data, and objective makes…
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…
Lynx: An Open Source Hallucination Evaluation Model
Selvan Sunitha Ravi, Bartosz Mielczarek, Anand Kannappan +2
Retrieval Augmented Generation (RAG) techniques aim to mitigate hallucinations in Large Language Models (LLMs). However, LLMs can still produce information that is unsupported or c…
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…
KTO: Model Alignment as Prospect Theoretic Optimization
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff +2
Kahneman & Tversky's tells us that humans perceive random variables in a biased but well-defined manner (1992); for example, humans are famously loss-ave…
I am a Strange Dataset: Metalinguistic Tests for Language Models
Tristan Thrush, Jared Moore, Miguel Monares +2
Statements involving metalinguistic self-reference ("This paper has six sections.") are prevalent in many domains. Can current large language models (LLMs) handle such language? In…