1 citations · 5 across the 18 of their papers we have counts for
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EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering
Xuanyu Meng, Jiashuo Sun, Jash Rajesh Parekh +1
Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmente…
Select-And-Extract: A Lightweight Plugin for Retrieval-Augmented Generation
Chenming Tang, Jiawei Han
Retrieval-augmented generation (RAG) for language model (LM) systems fundamentally has two failure modes: retrieval failure and reading failure. The former fails to recall the righ…
Learning to Predict Future-Aligned Research Proposals with Language Models
Heng Wang, Pengcheng Jiang, Jiashuo Sun +4
Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals remains difficult: novelty and sou…
GRACE: Generative Representation Learning via Contrastive Policy Optimization
Jiashuo Sun, Shixuan Liu, Zhaochen Su +6
Prevailing methods for training Large Language Models (LLMs) as text encoders rely on contrastive losses that treat the model as a black box function, discarding its generative and…
Topic Coverage-based Demonstration Retrieval for In-Context Learning
Wonbin Kweon, SeongKu Kang, Runchu Tian +3
The effectiveness of in-context learning relies heavily on selecting demonstrations that provide all the necessary information for a given test input. To achieve this, it is crucia…
Structure-Augmented Reasoning Generation
Jash Rajesh Parekh, Pengcheng Jiang, Jiawei Han
Recent advances in Large Language Models (LLMs) have significantly improved complex reasoning capabilities. Retrieval-Augmented Generation (RAG) has further extended these capabili…