Fetching the paper…
Reading the bibliography…
Retrieval-augmented generation (RAG) enhances LLMs with external knowledge, yet generation remains vulnerable to retrieval-induced noise and uncertain placement of relevant chunks, often causing hallucinations.
“Rouge: A package for automatic evaluation of summaries”
Chin-Yew Lin · 2004
Earlier work this paper cites.
“The probabilistic relevance framework: BM25 and beyond”
Stephen Robertson and Hugo Zaragoza · 2009
Earlier work this paper cites.
“MS-MARCO: A human generated machine reading comprehension dataset”
Payal Bajaj et al · 2016
Earlier work this paper cites.
“Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond”
Ramesh Nallapati et al · 2016
Earlier work this paper cites.
“Proximal policy optimization algorithms”
John Schulman et al · 2017
Earlier work this paper cites.
“A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents”
Arman Cohan et al · 2018
Earlier work this paper cites.
“HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering”
Zhilin Yang et al · 2018
Earlier work this paper cites.
“BERTScore: Evaluating Text Generation with BERT”
Tianyi Zhang et al · 2020
Earlier work this paper cites.
“MS-MARCO: Benchmarking ranking models in the large-data regime”
Nick Craswell et al · 2021
Earlier work this paper cites.
“Measuring Massive Multitask Language Understanding”
Dan Hendrycks et al · 2021
Earlier work this paper cites.
“Efficient Attentions for Long Document Summarization”
Luyang Huang et al · 2021
Earlier work this paper cites.
“Unsupervised Dense Information Retrieval with Contrastive Learning”
Gautier Izacard et al · 2021
Earlier work this paper cites.
“BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models”
Nandan Thakur et al · 2021
Earlier work this paper cites.
“Chain-of-thought prompting elicits reasoning in large language models”
Jason Wei et al · 2022
Earlier work this paper cites.
“A survey of chain of thought reasoning: Advances, frontiers and future”
Zheng Chu et al · 2023
Earlier work this paper cites.
“Precise Zero-Shot Dense Retrieval without Relevance Labels”
Luyu Gao, Xueguang Ma, Jimmy Lin and Jamie Callan · 2023
Earlier work this paper cites.
“Retrieval-augmented generation for large language models: A survey”
Yunfan Gao et al · 2023
Earlier work this paper cites.
“Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations”
Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu and Ming Yin · 2023
Earlier work this paper cites.
“Query2Doc: Query Expansion with Large Language Models”
Liang Wang, Nan Yang and Furu Wei · 2023
Earlier work this paper cites.
“Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection”
Akari Asai et al · 2024
Earlier work this paper cites.
“RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs”
Shreyas Chaudhari et al · 2024
Cited alongside, same era.
“The power of noise: Redefining retrieval for rag systems”
Florin Cuconasu et al · 2024
Cited alongside, same era.
“Under the surface: Tracking the artifactuality of llm-generated data”
Debarati Das et al · 2024
Cited alongside, same era.
“QLoRA: Efficient finetuning of quantized llms”
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman and Luke Zettlemoyer · 2024
Cited alongside, same era.
“KTO: Model alignment as prospect theoretic optimization”
Kawin Ethayarajh et al · 2024
Cited alongside, same era.
“Multilingual e5 text embeddings: A technical report”
Liang Wang et al · 2024
Later among the works it cites.
“A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More”
Zhichao Wang et al · 2024
Later among the works it cites.
“RECOMP: Improving retrieval-augmented lms with compression and selective augmentation”
Fangyuan Xu, Weijia Shi and Eunsol Choi · 2024
Later among the works it cites.
“Pride and prejudice: LLM amplifies self-bias in self-refinement”
Wenda Xu et al · 2024
Later among the works it cites.
“Corrective retrieval augmented generation”
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu and Zhen-Hua Ling · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“A survey on rag meeting llms: Towards retrieval-augmented large language models”
Wenqi Fan et al · 2024
Cited alongside, same era.
“Deliberative alignment: Reasoning enables safer language models”
Melody Guan et al · 2024
Cited alongside, same era.
“Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation”
Bolei He et al · 2024
Cited alongside, same era.
Taeho Hwang et al · 2024
Cited alongside, same era.
“Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models”
Shayekh Islam et al · 2024
Cited alongside, same era.
“Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems”
Philippe Laban, Alexander Fabbri, Caiming Xiong and Chien-Sheng Wu · 2024
Cited alongside, same era.
“Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation”
Zhuohang Li et al · 2024
Cited alongside, same era.
“Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment”
Rui Yang et al · 2024
Later among the works it cites.
“Rˆ 2AG: Incorporating Retrieval Information into Retrieval Augmented Generation”
Fuda Ye, Shuangyin Li, Yongqi Zhang and Lei Chen · 2024
Later among the works it cites.
“CompAct: Compressing Retrieved Documents Actively for Question Answering”
Chanwoong Yoon et al · 2024
Later among the works it cites.
“Evaluation of retrieval-augmented generation: A survey”
Hao Yu et al · 2024
Later among the works it cites.
“RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs”
Yue Yu et al · 2024
Later among the works it cites.
“Exploring the Best Practices of Query Expansion with Large Language Models”
Le Zhang, Yihong Wu, Qian Yang and Jian-Yun Nie · 2024
Later among the works it cites.
“Dense text retrieval based on pretrained language models: A survey”
Wayne Zhao, Jing Liu, Ruiyang Ren and Ji-Rong Wen · 2024
Later among the works it cites.
“Lima: Less is more for alignment”
Chunting Zhou et al · 2024
Later among the works it cites.
“Word2Passage: Word-level Importance Re-weighting for Query Expansion”
Jeonghwan Choi, Minjeong Ban, Minseok Kim and Hwanjun Song · 2025
Closest in time.
“Exit: Context-aware extractive compression for enhancing retrieval-augmented generation”
Taeho Hwang et al · 2025
Closest in time.
“Search-r1: Training llms to reason and leverage search engines with reinforcement learning”
Bowen Jin et al · 2025
Closest in time.
“Context embeddings for efficient answer generation in rag”
David Rau, Shuai Wang, Hervé Déjean and Stéphane Clinchant · 2025
Closest in time.
“Learning to Summarize from LLM-generated Feedback”
Hwanjun Song et al · 2025
Closest in time.
“Lighter and better: Towards flexible context adaptation for retrieval augmented generation”
Chenyuan Wu et al · 2025
Closest in time.