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When to Retrieve: Teaching LLMs to Utilize Information Retrieval
  Effectively

When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively

30 April 2024
Tiziano Labruna
Jon Ander Campos
Gorka Azkune
ArXivPDFHTML

Papers citing "When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively"

12 / 12 papers shown
Title
Agent-Enhanced Large Language Models for Researching Political Institutions
Agent-Enhanced Large Language Models for Researching Political Institutions
Joseph R. Loffredo
Suyeol Yun
LLMAG
77
0
0
14 Mar 2025
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
Ryan Barron
Maksim E. Eren
Olga M. Serafimova
Cynthia Matuszek
Boian S. Alexandrov
AILaw
78
0
0
27 Feb 2025
Quantifying Memorization and Retriever Performance in Retrieval-Augmented Vision-Language Models
Quantifying Memorization and Retriever Performance in Retrieval-Augmented Vision-Language Models
Peter Carragher
Abhinand Jha
R Raghav
Kathleen M. Carley
RALM
75
0
0
20 Feb 2025
SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models
SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models
Peter Carragher
Nikitha Rao
Abhinand Jha
R Raghav
Kathleen M. Carley
VLM
56
0
0
19 Feb 2025
Let your LLM generate a few tokens and you will reduce the need for
  retrieval
Let your LLM generate a few tokens and you will reduce the need for retrieval
Hervé Déjean
83
0
0
16 Dec 2024
Towards Reliable Medical Question Answering: Techniques and Challenges
  in Mitigating Hallucinations in Language Models
Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models
Duy Khoa Pham
Bao Quoc Vo
LM&MA
HILM
31
4
0
25 Aug 2024
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
To Eun Kim
Alireza Salemi
Andrew Drozdov
Fernando Diaz
Hamed Zamani
56
7
0
17 Jul 2024
To Generate or to Retrieve? On the Effectiveness of Artificial Contexts
  for Medical Open-Domain Question Answering
To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering
Giacomo Frisoni
Alessio Cocchieri
Alex Presepi
Gianluca Moro
Zaiqiao Meng
RALM
MedIm
49
15
0
04 Mar 2024
Two-Step Question Retrieval for Open-Domain QA
Two-Step Question Retrieval for Open-Domain QA
Yeon Seonwoo
Juhee Son
Jiho Jin
Sang-Woo Lee
Ji-Hoon Kim
Jung-Woo Ha
Alice H. Oh
RALM
LRM
32
5
0
19 May 2022
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information
  Retrieval Models
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Nandan Thakur
Nils Reimers
Andreas Rucklé
Abhishek Srivastava
Iryna Gurevych
VLM
231
971
0
17 Apr 2021
Retrieving and Reading: A Comprehensive Survey on Open-domain Question
  Answering
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Fengbin Zhu
Wenqiang Lei
Chao Wang
Jianming Zheng
Soujanya Poria
Tat-Seng Chua
RALM
213
252
0
04 Jan 2021
Scaling Laws for Neural Language Models
Scaling Laws for Neural Language Models
Jared Kaplan
Sam McCandlish
T. Henighan
Tom B. Brown
B. Chess
R. Child
Scott Gray
Alec Radford
Jeff Wu
Dario Amodei
246
4,489
0
23 Jan 2020
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