77 citations · 584 across the 84 of their papers we have counts for
10 papers · 1 filter
REFRAG: Rethinking RAG based Decoding
Xiaoqiang Lin, Aritra Ghosh, Bryan Kian Hsiang Low +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in leveraging extensive external knowledge to enhance responses in multi-turn and agentic applications, such…
Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble
Bingchen Wang, Zi-Yu Khoo, Jingtan Wang
Large language models are increasingly used as proxies for human subjects in social science research, yet external validity requires that synthetic agents faithfully reflect the pr…
Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation
Ze Yu Zhang, Zitao Li, Yaliang Li +2
Retrieval-augmented generation (RAG) based on large language models often falters on narrative documents with inherent temporal structures. Standard unstructured RAG methods rely s…
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents
Arun Verma, Indrajit Saha, Makoto Yokoo +1
This paper considers a contextual bandit problem involving multiple agents, where a learner sequentially observes the contexts and the agent's reported arms, and then selects the a…
ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
Xiaoqiang Lin, Arun Verma, Zhongxiang Dai +3
The recent success in using human preferences to align large language models (LLMs) has significantly improved their performance in various downstream tasks, such as question answe…
Active Human Feedback Collection via Neural Contextual Dueling Bandits
Arun Verma, Xiaoqiang Lin, Zhongxiang Dai +2
Collecting human preference feedback is often expensive, leading recent works to develop principled algorithms to select them more efficiently. However, these works assume that the…