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Towards bidirectional human-ai alignment: A systematic review for clarifications, framework, and future directions
Recent advancements in general-purpose AI have highlighted the importance of guiding AI
systems towards the intended goals, ethical principles, and values of individuals and …
systems towards the intended goals, ethical principles, and values of individuals and …
From persona to personalization: A survey on role-playing language agents
Recent advancements in large language models (LLMs) have significantly boosted the rise
of Role-Playing Language Agents (RPLAs), ie, specialized AI systems designed to simulate …
of Role-Playing Language Agents (RPLAs), ie, specialized AI systems designed to simulate …
Fine-tuning language models with advantage-induced policy alignment
B Zhu, H Sharma, FV Frujeri, S Dong, C Zhu… - arxiv preprint arxiv …, 2023 - arxiv.org
Reinforcement learning from human feedback (RLHF) has emerged as a reliable approach
to aligning large language models (LLMs) to human preferences. Among the plethora of …
to aligning large language models (LLMs) to human preferences. Among the plethora of …
The alignment ceiling: Objective mismatch in reinforcement learning from human feedback
N Lambert, R Calandra - arxiv preprint arxiv:2311.00168, 2023 - arxiv.org
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique
to make large language models (LLMs) more capable in complex settings. RLHF proceeds …
to make large language models (LLMs) more capable in complex settings. RLHF proceeds …
[PDF][PDF] Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions
D Jurgens - arxiv preprint arxiv:2406.09264, 2024 - 3dvar.com
Despite these numerous investigations into human-AI alignment, its definition and scope
remain ambiguous and inconsistent across literature, for example, regarding whom to align …
remain ambiguous and inconsistent across literature, for example, regarding whom to align …
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Interactive-Grounded Learning (IGL)[**e et al., 2021] is a powerful framework in which a
learner aims at maximizing unobservable rewards through interacting with an environment …
learner aims at maximizing unobservable rewards through interacting with an environment …
An information theoretic approach to interaction-grounded learning
Reinforcement learning (RL) problems where the learner attempts to infer an unobserved
reward from some feedback variables have been studied in several recent papers. The …
reward from some feedback variables have been studied in several recent papers. The …
Controllable Personalization for Information Access
S Mysore - 2024 - scholarworks.umass.edu
Abstract Information access systems mediate how we find and discover information in nearly
every walk of life. The ranking models powering these systems base their predictions on …
every walk of life. The ranking models powering these systems base their predictions on …
ICLR 2025 Workshop on Bidirectional Human-AI Alignment
As AI systems grow more integrated into real-world applications, the traditional one-way
approach to AI alignment is proving insufficient. Bidirectional Human-AI Alignment proposes …
approach to AI alignment is proving insufficient. Bidirectional Human-AI Alignment proposes …
[PDF][PDF] Human-Interactive Robot Learning: Definition, Challenges, and Recommendations
K Baraka, TK FAULKNER, E BIYIK, B SERENA… - 2018 - sannevw.github.io
Robot learning from humans has been proposed and researched for several decades as a
means to enable robots to learn new skills or adapt existing ones to new situations. Recent …
means to enable robots to learn new skills or adapt existing ones to new situations. Recent …