A survey that proposes the Generalist Virtual Agent concept and taxonomies for agent environments, tasks, perceptions, actions, models, and evaluation, concluding that real-world-like environments favor human-like intelligence.
AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models
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abstract
Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages. However, the efficiency of spliced prompts for such tasks remains underexplored. We introduces AllTogether, a standardized prompt template that enhances task context representation, thereby improving LLMs' performance in HTML-based web navigation. We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models. Our results reveal that models like GPT-4 outperform smaller models in web navigation tasks. Additionally, we find that the length of HTML snippet and history trajectory significantly influence performance, and prior step-by-step instructions prove less effective than real-time environmental feedback. Overall, we believe our work provides valuable insights for future research in LLM-driven web agents.
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cs.MA 1years
2024 1verdicts
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Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms
A survey that proposes the Generalist Virtual Agent concept and taxonomies for agent environments, tasks, perceptions, actions, models, and evaluation, concluding that real-world-like environments favor human-like intelligence.