From Artificial Needles to Real Haystacks: Improving Retrieval
Capabilities in LLMs by Finetuning on Synthetic Data

From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data

27 June 2024

Vasilis Papageorgiou

Dimitris Papailiopoulos

Papers citing "From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data"

7 / 7 papers shown

Title
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context Sangwon Yu Ik-hwan Kim Jongyoon Song Saehyung Lee Junsung Park Sungroh Yoon LRM 70 0 0 09 Oct 2024
How to Train Long-Context Language Models (Effectively) Tianyu Gao Alexander Wettig Howard Yen Danqi Chen RALM 72 38 0 03 Oct 2024
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? Zorik Gekhman G. Yona Roee Aharoni Matan Eyal Amir Feder Roi Reichart Jonathan Herzig 52 103 0 09 May 2024
Make Your LLM Fully Utilize the Context Shengnan An Zexiong Ma Zeqi Lin Nanning Zheng Jian-Guang Lou SyDa 51 54 0 25 Apr 2024
Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading Howard Chen Ramakanth Pasunuru Jason Weston Asli Celikyilmaz RALM 68 72 0 08 Oct 2023
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 367 8,495 0 28 Jan 2022
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 297 6,959 0 20 Apr 2018