Summary of Husky: a Unified, Open-source Language Agent For Multi-step Reasoning, by Joongwon Kim et al.
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
by Joongwon Kim, Bhargavi Paranjape, Tushar Khot, Hannaneh Hajishirzi
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Husky, a novel open-source language agent designed to tackle complex tasks involving numerical, tabular, and knowledge-based reasoning. Unlike existing proprietary models or task-specific agents, Husky learns to reason over a unified action space to address diverse tasks. The agent iterates between two stages: generating the next action to take towards solving a task and executing the action using expert models while updating the current solution state. To train expert models for executing these actions, the paper curates high-quality data and identifies a thorough ontology of actions for addressing complex tasks. Experimental results show that Husky outperforms prior language agents across 14 evaluation datasets, including the newly introduced HuskyQA set which stress-tests language agents for mixed-tool reasoning. Despite using 7B models, Husky matches or even exceeds frontier LMs like GPT-4 on these tasks, highlighting the effectiveness of the holistic approach in addressing complex reasoning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new kind of computer program called Husky that can help solve complicated problems. Husky is special because it’s not just good at one specific task – it can tackle many different kinds of problems that require thinking, math, and using knowledge from books or the internet. The researchers created Husky by giving it a set of rules to follow when trying to solve a problem. They also made sure Husky had access to lots of high-quality data to learn from. When tested against other computer programs, Husky performed well and was even better than some really powerful language models like GPT-4 on certain tasks. |
Keywords
» Artificial intelligence » Gpt
