Papers with alignment of language models
RewardBench: Evaluating Reward Models for Language Modeling (2025.findings-naacl)
Copied to clipboard
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, Lester James Validad Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi
| Challenge: | Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models. |
| Approach: | They present a benchmark dataset and code-base for evaluation of reward models . they use prompt-chosen-rejected trios to benchmark how they perform on queries . |
| Outcome: | The proposed dataset compares RMs with other models on a set of questions. |
MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to align large language models with human preferences often result in a static alignment that cannot account for the diversity of human preferences in practical applications. |
| Approach: | They propose a method to help large language models dynamically align with various explicit or implicit preferences specified at inference time. |
| Outcome: | The proposed method can help LLMs dynamically align with various explicit or implicit preferences specified at the inference stage, validating the feasibility of MetaAlign. |
Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback (2025.acl-long)
Copied to clipboard
Lester James Validad Miranda, Yizhong Wang, Yanai Elazar, Sachin Kumar, Valentina Pyatkin, Faeze Brahman, Noah A. Smith, Hannaneh Hajishirzi, Pradeep Dasigi
| Challenge: | Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. |
| Approach: | They propose a Hybrid Preference routER that defers an annotation to either humans or LMs, achieving better annotation quality while reducing the cost of human-only annotation. |
| Outcome: | The proposed model achieves better annotation quality while reducing the cost of human-only annotation. |
AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets. |
| Approach: | They propose a human summarization preference alignment framework to align PLMs with human preferences. |
| Outcome: | The proposed framework narrows the gap between automatic and human evaluations by integrating three components. |
Reward Model Perspectives: Whose Opinions Do Reward Models Reward? (2025.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that reward models are poorly aligned with demographic groups and can reward harmful stereotypes. |
| Approach: | They propose a framework for measuring the alignment of opinions captured by RMs . they also investigate the extent to which RM's demonstrate sociodemographic biases a . |
| Outcome: | The proposed framework measures the alignment of opinions captured by RMs . it shows that RM models are poorly aligned with several demographic groups . the findings highlight the need for more careful consideration of RM behavior in model alignment . |