Update README.md

README.md

@@ -13,28 +13,32 @@ tags:
 - planning
 ---
 
-# 🪄 Lumos: Language Agents with Unified Formats, Modular Design, and Open-Source LLMs
+# 🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
 <p align="center">
   🌐<a href="https://allenai.github.io/lumos">[Website]</a> &nbsp;
-  📝<a href="">[Paper]</a> &nbsp;
+  📝<a href="https://arxiv.org/abs/2311.05657">[Paper]</a> &nbsp;
   🤗<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a> &nbsp;
   🤗<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a> &nbsp;
+  🤗<a href="https://huggingface.co/spaces/ai2lumos/lumos_data_demo">[Demo]</a> &nbsp;
 </p>
 
 We introduce 🪄**Lumos**, Language Agents with **Unified** Formats, **Modular** Design, and **Open-Source** LLMs. **Lumos** unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents. 
 
 **Lumos** has following features:
 * 🧩 **Modular Architecture**:
-  - **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B.
+  - 🧩 **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B/13B and off-the-shelf APIs.
+  - 🤗 **Lumos** utilizes a unified data format that encompasses multiple task types, thereby enabling the developed agent framework to conveniently support a range of interactive tasks.
 * 🌍 **Diverse Training Data**:
-  - **Lumos** is trained with ~40K high-quality annotations from ground-truth reasoning steps in existing benchmarks with GPT-4. 
+  - 🌍 **Lumos** is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
+  - ⚒️ **Lumos** data can be instrumental for future research in developing open-source agents for complex interactive tasks.
 * 🚀 **Competitive Performance**:
-  - 🚀 **Lumos** outperforms **GPT-4/3.5-based** agents on complex QA and web agent tasks, and **larger open agents** on maths tasks.
-  - 🚀 **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **unmodularized** training.
-  - 🚀 **Lumos** surpasses larger open LLM agents and domain-specific agents on an unseen task, WebShop.
+  - 🚀 **Lumos** is comparable or even beats **GPT-series** agents on web/complex QA tasks Mind2Web and HotpotQA, and **larger open agents** on math and multimodal tasks.
+  - 🚀 **Lumos** exceeds contemporaneous agents that have been **fine-tuned** with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as **FiReAct**, **AgentLM**, and **AutoAct**.
+  - 🚀 **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **integrated** training.
+  - 🚀 **Lumos** surpasses larger open LLM agents and domain-specific agents on unseen tasks, WebShop and InterCode_SQL.
 
 ## Model Overview
-`lumos_unified_plan_iterative` is a **planning** module checkpoint finetuned on **complex QA**, **web agent** and **maths** tasks in **Lumos-Iterative (Lumos-I)** formulation. 
+`lumos_unified_plan_iterative` is a **planning** module checkpoint finetuned on **complex QA**, **web agent**, **multimodal** and **maths** tasks in **Lumos-Iterative (Lumos-I)** formulation. 
 
 The training annotation is shown below:
 
@@ -48,8 +52,9 @@ The training annotation is shown below:
 If you find this work is relevant with your research, please feel free to cite our work!
 ```
 @article{yin2023lumos,
-  title={Lumos: Towards Language Agents that are Unified, Modular, and Open Source},
+  title={Agent Lumos: Unified and Modular Training for Open-Source Language Agents},
   author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
+  journal={https://arxiv.org/abs/2311.05657},
   year={2023}
 }
 ```