120 citations · 196 across the 8 of their papers we have counts for
6 papers · 1 filter
HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation
Cristina Garbacea, Chenhao Tan
Alignment algorithms are widely used to align large language models (LLMs) to human users based on preference annotations. Typically these (often divergent) preferences are aggrega…
RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
David Reber, Sean Richardson, Todd Nief +2
Reward models are widely used as proxies for human preferences when aligning or evaluating LLMs. However, reward models are black boxes, and it is often unclear what, exactly, they…
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling
Lin Gui, Cristina Gârbacea, Victor Veitch
This paper concerns the problem of aligning samples from large language models to human preferences using best-of- sampling, where we draw samples, rank them, and return the…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Neural Language Generation: Formulation, Methods, and Evaluation
Cristina Garbacea, Qiaozhu Mei
Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand…
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
Cristina Garbacea, Samuel Carton, Shiyan Yan +1
We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compa…