Search Results for author:
Found 13 papers, 8 papers with code
360°REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
The framework employs a novel 360{\deg} performance assessment method for multi-perspective performance evaluation with fine-grained assessment.
StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation
Furthermore, although many prompt-based methods have been proposed to accomplish specific tasks, their performance in complex real-world scenarios involving a wide variety of dialog styles further enhancement.
StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses
Accordingly, we introduce StreamingDialogue, which compresses long dialogue history into conv-attn sinks with minimal losses, and thus reduces computational complexity quadratically with the number of sinks (i. e., the number of utterances).
Generative News Recommendation
In this paper, we propose a novel generative news recommendation paradigm that includes two steps: (1) Leveraging the internal knowledge and reasoning capabilities of the Large Language Model (LLM) to perform high-level matching between candidate news and user representation; (2) Generating a coherent and logically structured narrative based on the associations between related news and user interests, thus engaging users in further reading of the news.
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas.
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation
Recently, the advent of large language models (LLMs) has revolutionized generative agents.
RoleEval: A Bilingual Role Evaluation Benchmark for Large Language Models
We expect that RoleEval would highlight the significance of assessing role knowledge for large language models across various languages and cultural settings.
Are We Falling in a Middle-Intelligence Trap? An Analysis and Mitigation of the Reversal Curse
Recent studies have highlighted a phenomenon in large language models (LLMs) known as "the reversal curse," in which the order of knowledge entities in the training data biases the models' comprehension.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales.
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
With in-context learning (ICL) as the core of the cycle, the black-box models are able to rank the model-generated responses guided by human-craft instruction and demonstrations about their preferences.
CharacterChat: Learning towards Conversational AI with Personalized Social Support
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
SSP: Self-Supervised Post-training for Conversational Search
Conversational search has been regarded as the next-generation search paradigm.
MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress.
