BOOKMARKS introduces searchable bookmarks as reusable answers to storyline questions, enabling active initialization and passive synchronization for more consistent role-playing agent memory than recurrent summarization.
InProceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 13153–13187, Singapore
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
verdicts
UNVERDICTED 6roles
method 1polarities
use method 1representative citing papers
RoleJudge is a multidimensional evaluation framework for speech-character alignment in audio LLMs, backed by the RoleChat dataset and multi-stage RL training with standard alignment to reduce reward issues.
Conditioning on character arcs improves role-playing language agents' performance over other context strategies, with largest gains on scenarios outside the source text.
New benchmark RoleCDE reveals LLMs exhibit role value decoupling under conflicts and demonstrates mitigation via targeted fine-tuning.
PersonaArena is a dynamic simulation framework that constructs persona banks from social data and uses multi-agent debating judges to evaluate and enhance persona-level role-playing in LLMs.
Introduces a mitigation technique that drops LLM accuracy on popular fiction character tasks from 96% to 72% by limiting verbatim memorization while retaining gist cues.
citing papers explorer
-
BOOKMARKS: Efficient Active Storyline Memory for Role-playing
BOOKMARKS introduces searchable bookmarks as reusable answers to storyline questions, enabling active initialization and passive synchronization for more consistent role-playing agent memory than recurrent summarization.
-
Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning
RoleJudge is a multidimensional evaluation framework for speech-character alignment in audio LLMs, backed by the RoleChat dataset and multi-stage RL training with standard alignment to reduce reward issues.
-
ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?
Conditioning on character arcs improves role-playing language agents' performance over other context strategies, with largest gains on scenarios outside the source text.
-
RoleCDE:Benchmarking and Mitigating Role-Alignment Trade-offs in Role-Playing Agents
New benchmark RoleCDE reveals LLMs exhibit role value decoupling under conflicts and demonstrates mitigation via targeted fine-tuning.
-
PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models
PersonaArena is a dynamic simulation framework that constructs persona banks from social data and uses multi-agent debating judges to evaluate and enhance persona-level role-playing in LLMs.
-
Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs
Introduces a mitigation technique that drops LLM accuracy on popular fiction character tasks from 96% to 72% by limiting verbatim memorization while retaining gist cues.