{"total":17,"rows":[{"id":"2504.12501","title":"Reinforcement Learning from Human Feedback","created":"2025-04-16","cited_by_count":58,"area":"alignment"},{"id":"1805.10627","title":"Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning","created":"2018-05-27","cited_by_count":57,"area":"alignment"},{"id":"2307.16851","title":"Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives","created":"2023-07-31","cited_by_count":30,"area":"alignment"},{"id":"2406.11191","title":"A Survey on Human Preference Learning for Large Language Models","created":"2024-06-17","cited_by_count":24,"area":"alignment"},{"id":"2503.02846","title":"Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs","created":"2025-03-04","cited_by_count":24,"area":"alignment"},{"id":"2310.15477","title":"CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model","created":"2023-10-24","cited_by_count":5,"area":"alignment"},{"id":"2409.17169","title":"REAL: Response Embedding-based Alignment for LLMs","created":"2024-09-17","cited_by_count":4,"area":"alignment"},{"id":"2512.11838","title":"D-STEER - Preference Alignment Techniques Learn to Behave, not to Believe -- Beneath the Surface, DPO as Steering Vector Perturbation in Activation Space","created":"2025-12-03","cited_by_count":2,"area":"alignment"},{"id":"2408.16751","title":"A Gradient Analysis Framework for Rewarding Good and Penalizing Bad Examples in Language Models","created":"2024-08-29","cited_by_count":1,"area":"alignment"},{"id":"2412.04922","title":"Large Language Models for Ingredient Substitution in Food Recipes using Supervised Fine-tuning and Direct Preference Optimization","created":"2024-12-06","cited_by_count":1,"area":"alignment"},{"id":"2510.07557","title":"Investigating Thematic Patterns and User Preferences in LLM Interactions using BERTopic","created":"2025-10-08","cited_by_count":1,"area":"alignment"},{"id":"2510.19562","title":"DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning","created":"2025-10-22","cited_by_count":1,"area":"alignment"},{"id":"1809.01036","title":"A Roadmap for Robust End-to-End Alignment","created":"2018-09-04","cited_by_count":0,"area":"alignment"},{"id":"2603.04429","title":"What Is Missing: Interpretable Ratings for Large Language Model Outputs","created":"2026-02-17","cited_by_count":0,"area":"alignment"},{"id":"2605.07172","title":"Topology-Enhanced Alignment for Large Language Models: Trajectory Topology Loss and Topological Preference Optimization","created":"2026-05-08","cited_by_count":0,"area":"alignment"},{"id":"2607.05401","title":"Catalyst Papers in Artificial Intelligence Research: A Landscape on ICLR from 2017 to 2025","created":"2026-05-24","cited_by_count":0,"area":"alignment"},{"id":"2609.04022","title":"Representational alignment yields generalizable safety in language models","created":"2026-09-03","cited_by_count":0,"area":"alignment"}],"next_cursor":null}