Skip to content

Code for Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks (WWW 2024))

Notifications You must be signed in to change notification settings

xsc1234/Search-in-the-Chain

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

This is the project for Search-in-the-Chain

Welcome to read our paper:https://arxiv.org/abs/2304.14732

@inproceedings{xu2024search,
  title={Search-in-the-chain: Interactively enhancing large language models with search for knowledge-intensive tasks},
  author={Xu, Shicheng and Pang, Liang and Shen, Huawei and Cheng, Xueqi and Chua, Tat-Seng},
  booktitle={Proceedings of the ACM Web Conference 2024},
  pages={1362--1373},
  year={2024}
}

You can start Searchain quickly from LLamaIndex: here

You can try to run our project by following the steps below, running in different environments may encounter various problems. We are still working hard to make it robust and bug-free.

1. Index your corpus via ColBERT

Process your data into a format suitable for ColBERT indexing

python ColBERT/process_hotpotqa_wiki.py

Indext your data

python ColBERT/index.py

Run the service for retrieval

python ColBERT/server_retrieval.py

2. Run the serive for verification and completion in Information Retrieval

python Server/server.py

3. Construct Chain-of-Query and and interact with search service (communicate with Server/server.py)

An example on HotpotQA in the setting without IR:

python SearChain_without_IR.py

An example on HotpotQA in the setting with IR:

python SearChain_w_IR.py

About

Code for Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks (WWW 2024))

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •