Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
- URL: http://arxiv.org/abs/2406.06469v1
- Date: Mon, 10 Jun 2024 17:07:25 GMT
- Title: Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
- Authors: Joongwon Kim, Bhargavi Paranjape, Tushar Khot, Hannaneh Hajishirzi,
- Abstract summary: We introduce Husky, a holistic, open-source language agent that learns to reason over a unified action space.
Husky iterates between two stages: 1) generating the next action to take towards solving a given task and 2) executing the action using expert models.
Our experiments show that Husky outperforms prior language agents across 14 evaluation datasets.
- Score: 67.26776442697184
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Language agents perform complex tasks by using tools to execute each step precisely. However, most existing agents are based on proprietary models or designed to target specific tasks, such as mathematics or multi-hop question answering. We introduce Husky, a holistic, open-source language agent that learns to reason over a unified action space to address a diverse set of complex tasks involving numerical, tabular, and knowledge-based reasoning. Husky iterates between two stages: 1) generating the next action to take towards solving a given task and 2) executing the action using expert models and updating the current solution state. We identify a thorough ontology of actions for addressing complex tasks and curate high-quality data to train expert models for executing these actions. Our experiments show that Husky outperforms prior language agents across 14 evaluation datasets. Moreover, we introduce HuskyQA, a new evaluation set which stress tests language agents for mixed-tool reasoning, with a focus on retrieving missing knowledge and performing numerical reasoning. Despite using 7B models, Husky matches or even exceeds frontier LMs such as GPT-4 on these tasks, showcasing the efficacy of our holistic approach in addressing complex reasoning problems. Our code and models are available at https://github.com/agent-husky/Husky-v1.
Related papers
- DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs [70.54226917774933]
We propose the DecompositionAlignment-Reasoning Agent (DARA) framework.
DARA effectively parses questions into formal queries through a dual mechanism.
We show that DARA attains performance comparable to state-of-the-art enumerating-and-ranking-based methods for KGQA.
arXiv Detail & Related papers (2024-06-11T09:09:37Z) - Plan of Thoughts: Heuristic-Guided Problem Solving with Large Language Models [0.0]
We formalize a planning-based approach to perform multi-step problem solving with language models.
We demonstrate a superior success rate of 89.4% on the Game of 24 task as compared to existing approaches.
arXiv Detail & Related papers (2024-04-29T18:51:17Z) - Retrieval-Generation Synergy Augmented Large Language Models [30.53260173572783]
We propose an iterative retrieval-generation collaborative framework.
We conduct experiments on four question answering datasets, including single-hop QA and multi-hop QA tasks.
arXiv Detail & Related papers (2023-10-08T12:50:57Z) - JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for
Multi-task Mathematical Problem Solving [77.51817534090789]
We propose textbfJiuZhang2.0, a unified Chinese PLM specially for multi-task mathematical problem solving.
Our idea is to maintain a moderate-sized model and employ the emphcross-task knowledge sharing to improve the model capacity in a multi-task setting.
arXiv Detail & Related papers (2023-06-19T15:45:36Z) - UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question
Answering Over Knowledge Graph [89.98762327725112]
Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question.
We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
arXiv Detail & Related papers (2022-12-02T04:08:09Z) - ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational
Finance Question Answering [70.6359636116848]
We propose a new large-scale dataset, ConvFinQA, to study the chain of numerical reasoning in conversational question answering.
Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations.
arXiv Detail & Related papers (2022-10-07T23:48:50Z) - ReAct: Synergizing Reasoning and Acting in Language Models [44.746116256516046]
We show that large language models (LLMs) can generate both reasoning traces and task-specific actions in an interleaved manner.
We apply our approach, named ReAct, to a diverse set of language and decision making tasks.
ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API.
arXiv Detail & Related papers (2022-10-06T01:00:32Z) - XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation [80.18830380517753]
We develop a new task-agnostic distillation framework XtremeDistilTransformers.
We study the transferability of several source tasks, augmentation resources and model architecture for distillation.
arXiv Detail & Related papers (2021-06-08T17:49:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.