Logo

GenAudit 事实核查LLM输出校正 VS Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration

GenAudit 事实核查LLM输出校正与Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration对比，GenAudit 事实核查LLM输出校正与Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration有什么不同？

GenAudit 事实核查LLM输出校正

访问官网

什么是GenAudit

GenAudit是一个旨在帮助校验大型语言模型（LLM）在文档支持任务中的响应的工具。它可以建议对LLM响应进行编辑，通过修正或移除未被参考文档支持的声明，并且为看似有支持的事实提供参考证据。

GenAudit的功能亮点

1. 对LLM生成的文本进行事实核查；2. 修正或移除与参考文档不一致的声明；3. 为有支持的事实提供参考文档中的证据；4. 提供交互式用户界面以便于用户进行事实核查。
GenAudit通过训练模型执行核查任务，能够准确发现不一致的声明和提供支持的参考证据，提高了文本准确性和可信度。

GenAudit的使用案例

1. 医疗领域的研究人员使用GenAudit校验由LLM生成的病历摘要；2. 金融分析师利用GenAudit确保由LLM提供的财务报告摘要的准确性；3. 编辑和作者使用GenAudit来提高他们出版物中事实内容的准确性。

使用GenAudit的好处

帮助用户提高文本准确性、增加文档的可信度、避免错误信息传播。

GenAudit的局限性

目前仅局限于支持文档核查任务，且可能受限于特定领域的模型训练成果。

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration

Train LLMs to reason and call search engines efficiently using reinforcement learning
访问官网

什么是Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration

Search-R1 is a powerful reinforcement learning framework designed for training language models (LLMs) that can reason and make tool calls—such as to search engines—in a coordinated manner. It builds on the concepts of DeepSeek-R1(-Zero) and incorporates cutting-edge tools like veRL, a reinforcement learning library that facilitates efficient training of models with complex tool interactions. This framework allows LLMs to access external information via search engines, boosting their ability to handle reasoning tasks dynamically and effectively.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration怎么用？

To use Search-R1, follow these steps: 1. Set up the environment using the provided conda commands and install necessary libraries like PyTorch, vLLM, and Flash Attention. 2. Train an LLM (e.g., Llama3 or Qwen2.5) with reinforcement learning methods like PPO. 3. Use your own dataset or pre-built datasets for training. 4. Integrate local or online search engines and make sure the LLM can call these engines during training for information retrieval. 5. Run the model on the inference server and ask the trained model questions to observe its reasoning ability in real-time.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration核心功能

  • Search-R1 offers a range of powerful features:
  • Support for local sparse and dense retrievers (BM25, ANN, etc.)
  • Integration with major search engines like Google and Bing
  • Flexible RL methods (PPO, GRPO, reinforce)
  • Compatibility with various LLMs (e.g., Llama3, Qwen2.5)
  • Open-source RL training pipeline for easy customization and experimentation

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration使用案例

  • Here are some example use cases for Search-R1:
  • Train a reasoning-based LLM using the NQ dataset, integrating the E5 retriever and Wikipedia corpus for real-world information retrieval.
  • Conduct multi-turn reasoning tasks where the model interacts with search engines and refines its answers based on subsequent search results.
  • Implement a custom search engine setup for specialized domain-specific tasks and incorporate it into the RL training loop.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration价格

Search-R1 is an open-source project, and its codebase can be freely accessed on GitHub. The cost of using it is minimal for small-scale training but may scale with larger datasets and LLMs. For instance, large models like 30B+ parameter LLMs can incur additional computational costs, particularly when running distributed training across multiple nodes.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration公司名称

Search-R1 is developed and maintained by PeterGriffinJin, a contributor to open-source machine learning research.

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration联系方式

For inquiries, you can reach the Search-R1 team at the email address: [email protected].

Search-R1: Efficient RL Training Framework for LLMs with Search Engine Integration社交媒体

Stay connected with the Search-R1 team on social media: Twitter: @PeterGriffinJin Instagram: @petergriffinjin