Datasets:
dataset_info:
features:
- name: query_id
dtype: int64
- name: query
dtype: string
- name: document
dtype: string
splits:
- name: retail
num_bytes: 16261464
num_examples: 5000
- name: videogames
num_bytes: 7786542
num_examples: 4360
- name: books
num_bytes: 2858945
num_examples: 2245
- name: news
num_bytes: 11619385
num_examples: 2375
- name: web
num_bytes: 17871918
num_examples: 1500
- name: debate
num_bytes: 10085407
num_examples: 880
download_size: 33921309
dataset_size: 66483661
configs:
- config_name: default
data_files:
- split: retail
path: data/retail-*
- split: videogames
path: data/videogames-*
- split: books
path: data/books-*
- split: news
path: data/news-*
- split: web
path: data/web-*
- split: debate
path: data/debate-*
language:
- en
license: apache-2.0
tags:
- SEO
- CSEO
- RAG
- conversational-search-engine
Dataset Summary
C-SEO Bench is a benchmark designed to evaluate conversational search engine optimization (C-SEO) techniques across two common tasks: product recommendation and question answering. Each task spans multiple domains to assess domain-specific effects and generalization ability of C-SEO methods.
Supported Tasks and Domains
Product Recommendation
This task requires an LLM to recommend the top-k products relevant to a user query, using only the content of 10 retrieved product descriptions. The task simulates a cold-start setting with no user profile. Domains:
- Retail: Queries and product descriptions from Amazon.
- Video Games: Search tags and game descriptions from Steam.
- Books: GPT-generated queries with book synopsis from the Google Books API.
Question Answering
This task involves answering queries based on multiple passages. Domains:
- Web Questions: Real search engine queries with retrieved web content.
- News: GPT-generated questions over sets of related news articles.
- Debate: Opinionated queries requiring multi-perspective evidence.
Total: Over 1.9k queries and 16k documents across six domains.
For more information about the dataset construction, please refer to the original publication.
Developed at Parameter Lab with the support of Naver AI Lab.
Disclaimer
This repository contains experimental software results and is published for the sole purpose of giving additional background details on the respective publication.
Citation
If this work is useful for you, please consider citing it
TODO
✉️ Contact person: Haritz Puerto, [email protected]
🏢 https://www.parameterlab.de/
Don't hesitate to send us an e-mail or report an issue if something is broken (and it shouldn't be) or if you have further questions.