RankGPT

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RankGPT is an open-source research tool that investigates large language models as re-ranking agents for information retrieval. It provides code and resources for exploring generative LLMs like ChatGPT in search applications.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is RankGPT?

RankGPT is an open-source project dedicated to exploring the capabilities of large language models (LLMs), such as ChatGPT and GPT-4, as re-ranking agents within information retrieval systems. The project provides code and resources for researchers to investigate how these generative LLMs can improve relevance ranking. It features instructional permutation generation for re-ranking passages, including a sliding window strategy to handle token limits. RankGPT also supports the distillation of LLMs into smaller, specialized models for efficiency and offers evaluation benchmarks for various datasets like TREC, BEIR, and Mr. TyDi. The project recently won the Outstanding Paper Award at EMNLP 2023.

Best used for

Ideal for professors and researchers who need to investigate the effectiveness of large language models in re-ranking search results, develop more efficient specialized ranking models, and evaluate their performance against established benchmarks. Especially valuable for advancing the field of information retrieval and LLM applications.

Common actions

re-rank search results

evaluate LLM performance

distill LLM models

research information retrieval

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Capabilities

Key features

LLM re-ranking agent
Instructional permutation generation
Sliding window strategy
LLM distillation
Benchmark evaluation
Supports various LLMs

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary goal of the RankGPT project?

The RankGPT project aims to explore how generative large language models, such as ChatGPT and GPT-4, can be effectively utilized as re-ranking agents to improve the relevance of search results in information retrieval systems. It provides tools and methodologies for this investigation.

How does RankGPT handle the token limits of LLMs for re-ranking many passages?

RankGPT employs a sliding window strategy for instructional permutation generation. This approach allows LLMs to rank more passages than their maximum token limit by processing and re-ranking subsets of passages within a defined window, moving from back to front.

Can RankGPT be used to create smaller, specialized ranking models?

Yes, RankGPT supports the distillation of large language models into smaller, specialized models. This process helps in achieving state-of-the-art ranking performances with open-source LLMs, enhancing efficiency and potentially reducing computational costs for specific tasks.

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AI Agents & Automation › General-Purpose Agents

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