1 citations · 3 across the 11 of their papers we have counts for
9 papers · 1 filter
FARCA: Fact-Aligned Reliability-Aware Credit Assignment for Reinforcement Learning with Factual Supervision
Qiming Xie, Wenjie Zheng, Xiangqing Shen +1
To reduce the hallucination risk caused by outcome-driven rewards in large language models trained through reinforcement learning with verifiable rewards, existing mitigation appro…
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Yuhang Wu, Xiangqing Shen, Fanfan Wang +4
Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated…
ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang +1
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdi…
Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQA
Xiangqing Shen, Fanfan Wang, Rui Xia
LLMs have demonstrated remarkable capabilities in complex reasoning tasks, yet they often suffer from hallucinations and lack reliable factual grounding. Meanwhile, knowledge graph…
MEMIT-Merge: Addressing MEMIT's Key-Value Conflicts in Same-Subject Batch Editing for LLMs
Zilu Dong, Xiangqing Shen, Rui Xia
As large language models continue to scale up, knowledge editing techniques that modify models' internal knowledge without full retraining have gained significant attention. MEMIT,…
A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions
Siwei Wu, Xiangqing Shen, Rui Xia
Large Language Models (LLMs), such as ChatGPT, have recently been applied to various NLP tasks due to its open-domain generation capabilities. However, there are two issues with ap…
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering
Siwei Wu, Xiangqing Shen, Rui Xia
The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This le…
Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines
Yaochen Zhu, Xiangqing Shen, Rui Xia
Personality traits, emotions, and beliefs shape individuals' behavioral choices and decision-making processes. However, for one thing, the affective computing community normally fo…
Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths
Xiangqing Shen, Siwei Wu, Rui Xia
ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., {head event, relation, tail event}. The one-hop annotation manner m…