Awesome
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
This repo provides the source code & data of our paper: Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models (Arxiv 2023).
😀 Overview
Highlights:
- 1️⃣ We are the first to examine ChatGPT in conversational recommendation systematically, the ability of which is underestimated in traditional evaluation approach.
- 💡 We propose a new interactive approach that employs LLM-based user simulators for evaluating CRSs.
- 🔝 The recall@50 metric can be boosted from 0.218 to 0.739 on the redial dataset with our new interactive evaluation approach, even surpassing the currently leading CRS baseline.
we propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators. We take the ground-truth items from the example as the user preference through the interaction, and use them to set up the persona of the simulated users by LLMs through instructions. To further make a comprehensive evaluation, we consider two types of interaction: attribute-based question answering and free-form chit-chat.
<p align="center"> <img src="./asset/eval.png" width="75%" height="75% title="Overview of iEvaLM-CRS" alt=""> </p>🚀 Quick Start
Requirements
- python == 3.9.16
- pytorch == 1.13.1
- transformers == 4.28.1
- pyg == 2.3.0
- accelerate == 0.18.0
Download Models
You can download our fine-tuned models from the link, which include recommendation and conversation models of KBRD, BARCOR and UniCRS. Please put the downloaded model into src/utils/model directory.
Interact with the user simulator
- dataset: [redial, opendialkg]
- mode: [ask, chat]
- model: [kbrd, barcor, unicrs, chatgpt]
cd script
bash {dataset}/cache_item.sh
bash {dataset}/{mode}_{model}.sh
You can customize your iEvaLM-CRS by specifying these configs:
--api_key: your API key--turn_num: number of conversation turns. We employ five-round interaction in iEvaLM-CRS.
After the execution, you will find detailed interaction information under "save_{turn_num}/{mode}/{model}/{dataset}/".
Evaluate
cd script
bash {dataset}/Rec_eval.sh
You can customize your iEvaLM-CRS by specifying these configs:
--turn_num: number of conversation turns.--mode: [ask, chat]
After the execution, you will find evaluation results under "save_{turn_num}/result/{mode}/{model}/{dataset}.json".
🌟 Perfermance
<p align="center">Performance of CRSs and ChatGPT using different evaluation approaches.</p> <table border="1" align="center"> <tbody > <tr align="center"> <td colspan="2">Model</td> <td colspan="3">KBRD</td> <td colspan="3">BARCOR</td> <td colspan="3">UniCRS</td> <td colspan="3">ChatGPT</td> </tr> <tr align="center"> <td colspan="2">Evaluation Approach</td> <td>Original</td> <td>iEvaLM(attr)</td> <td>iEvaLM(free)</td> <td>Original</td> <td>iEvaLM(attr)</td> <td>iEvaLM(free)</td> <td>Original</td> <td>iEvaLM(attr)</td> <td>iEvaLM(free)</td> <td>Original</td> <td>iEvaLM(attr)</td> <td>iEvaLM(free)</td> </tr> <tr align="center"> <td rowspan="3">ReDial</td> <td>R@1</td> <td>0.028</td> <td>0.039</td> <td>0.035</td> <td>0.031</td> <td>0.034</td> <td>0.034</td> <td>0.050</td> <td>0.053</td> <td>0.107</td> <td>0.037</td> <td><b>0.191</b></td> <td>0.146</td> </tr> <tr align="center"> <td>R@10</td> <td>0.169</td> <td>0.196</td> <td>0.198</td> <td>0.170</td> <td>0.201</td> <td>0.190</td> <td>0.215</td> <td>0.238</td> <td>0.317</td> <td>0.174</td> <td><b>0.536</b></td> <td>0.440</td> </tr> <tr align="center"> <td>R@50</td> <td>0.366</td> <td>0.436</td> <td>0.453</td> <td>0.372</td> <td>0.427</td> <td>0.467</td> <td>0.413</td> <td>0.520</td> <td>0.602</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr align="center"> <td rowspan="3">OpenDialKG</td> <td>R@1</td> <td>0.231</td> <td>0.131</td> <td>0.234</td> <td>0.312</td> <td>0.264</td> <td>0.314</td> <td>0.308</td> <td>0.180</td> <td>0.314</td> <td>0.310</td> <td>0.299</td> <td><b>0.400</b></td> </tr> <tr align="center"> <td>R@10</td> <td>0.423</td> <td>0.293</td> <td>0.431</td> <td>0.453</td> <td>0.423</td> <td>0.458</td> <td>0.513</td> <td>0.393</td> <td>0.538</td> <td>0.539</td> <td>0.604</td> <td><b>0.715</b></td> </tr> <tr align="center"> <td>R@50</td> <td>0.492</td> <td>0.377</td> <td>0.509</td> <td>0.510</td> <td>0.482</td> <td>0.530</td> <td>0.574</td> <td>0.458</td> <td>0.609</td> <td>-</td> <td>-</td> <td>-</td> </tr> </tbody> </table> <p align="center">Persuasiveness of explanations generated by CRSs and ChatGPT.</p> <table border="1" align="center"> <tbody> <tr align="center"> <td>Model</td> <td>Evaluation Approach</td> <td>ReDial</td> <td>OpenDialKG</td> </tr> <tr align="center"> <td rowspan="2">KBRD</td> <td>Original</td> <td>0.638</td> <td>0.824</td> </tr> </tr> <tr align="center"> <td>iEvaLM</td> <td>0.766</td> <td>0.862</td> </tr> </tr> <tr align="center"> <td rowspan="2">BARCOR</td> <td>Original</td> <td>0.667</td> <td>1.149</td> </tr> </tr> <tr align="center"> <td>iEvaLM</td> <td>0.795</td> <td>1.211</td> </tr> </tr> <tr align="center"> <td rowspan="2">UniCRS</td> <td>Original</td> <td>0.685</td> <td>1.128</td> </tr> </tr> <tr align="center"> <td>iEvaLM</td> <td>1.015</td> <td>1.314</td> </tr> </tr> <tr align="center"> <td rowspan="2">ChatGPT</td> <td>Original</td> <td>0.787</td> <td>1.221</td> </tr> </tr> <tr align="center"> <td>iEvaLM</td> <td><b>1.331</b></td> <td><b>1.513</b></td> </tr> </tbody> </table>CRSLab will support this interactive evaluation approach, the results in our paper will be updated soon.
📮 Contact
If you have any questions for our paper or codes, please send an email to txy20010310@163.com.
🐦 Citing
Please cite the following paper if you find our code helpful.
@article{wang2023rethinking,
title={Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models},
author={Wang, Xiaolei and Tang, Xinyu and Zhao, Wayne Xin and Wang, Jingyuan and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2305.13112},
year={2023}
}