Search Results for author:
Found 11 papers, 2 papers with code
A Survey of Personalized Large Language Models: Progress and Future Directions
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences.
NILE: Internal Consistency Alignment in Large Language Models
However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT.
Purple-teaming LLMs with Adversarial Defender Training
In PAD, we automatically collect conversational data that cover the vulnerabilities of an LLM around specific safety risks in a self-play manner, where the attacker aims to elicit unsafe responses and the defender generates safe responses to these attacks.
Mitigating Large Language Model Hallucination with Faithful Finetuning
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks.
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation
By combining the reasoning capabilities of LLMs with the effectiveness of tree search, SeRTS boosts the zero-shot performance of retrieving high-quality and informative results for RAG.
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs.
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
To seamlessly integrate both modalities, we introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent.
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses.
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration
Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question-answering tasks.
Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?
Making moral judgments is an essential step toward developing ethical AI systems.
Momentum Contrastive Pre-training for Question Answering
In order to address this problem, we propose a novel Momentum Contrastive pRe-training fOr queStion anSwering (MCROSS) method for extractive QA.
