Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents (2024)

Abstract: In this paper, we introduce Auto-Intent, a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning, where we empirically focus on web navigation tasks. Our approach first discovers the underlying intents from target domain demonstrations unsupervisedly, in a highly compact form (up to three words). With the extracted intents, we train our intent predictor to predict the next intent given the agent’s past observations and actions. In particular, we propose a self-exploration approach where top-k probable intent predictions are provided as a hint to the pre-trained LLM agent, which leads to enhanced decision-making capabilities. Auto-Intent substantially improves the performance of GPT-3.5, 4 and Llama-3.1-70B, 405B agents on the large-scale real-website navigation benchmarks from Mind2Web and online navigation tasks from WebArena with its cross-benchmark generalization from Mind2Web.

## introduction recently, large language models (llms) pretrained on a massive amount of data @xcite have excelled at reasoning and a variety of tasks. they exhibit robust planning and reasoning abilities, enabling llm agents to perform diverse tasks @xcite @xcite . however, these agents often face challenges in domains with less prior knowledge, especially ones with large action spaces, such as navigating websites or operating mobile devices @xcite @xcite . we explore improving decision-making with pretrained llms on downstream tasks by injecting domain knowledge into the input context, in the form of natural language hints for the next action. this allows them to fully retain their strong general reasoning capabilities while avoiding overly costly figure 1 : overview of auto-intent: given a dataset of demonstration trajectories, we first extract natural language intents in an unsupervised manner and train an intent predictor. enforcing the intents to be concise phrases and providing top-k intent predictions as hints to an llm agent allows efficient internal exploration of semantically diverse intent hypotheses, resulting in improved action prediction. see text for details. or impossible fine-tuning. leveraging natural language guidance for improving llm planning and reasoning capabilities has found much success in prior work @xcite @xcite @xcite . although prior work has shown that llms have strong priors to reason about intermediate subgoals @xcite @xcite , the resulting performance can be largely affected by the injected hints' accuracy, which could be limited especially in complex environments such as real-world web navigation with numerous elements and possible actions. in this work, we aim to improve the llm agent's performance further by proposing self-exploration. our key insight is to provide multiple plausible and semantically varied hints that we call intents to the llm agent for flexible reasoning and acting given a set of possible directions. to achieve this, we constrain intents to very short phrases and generate top-k intents to provide as a collective hint with a beam search using a smaller model fine-tuned for intent prediction. this fine-tuning is enabled by discovering intents from demonstration data with our intent extractor. the compact intent space encourages semantically distinct intents to be sampled (as opposed to syntactically diverse intents that are semantically identical). this self-exploration with multiple intents enhances the agent to find the correct directions and associated actions. see figure 1 for an illustration of our approach. our main contributions are as follows: • we introduce auto-intent, a method to extract natural language intents from demonstration trajectories in an unsupervised manner and leverage intents as hints for pre-trained llm agents through a fine-tuned intent prediction model. ## auto-intent: intent discovery and self-exploration with intent prediction to address the inadequate domain knowledge in pre-trained llm agents, we introduce an abstract natural language representation we refer to as an intent, which hints what the agent can perform next. we aim to enhance llm agents further without limiting them by the intent prediction model's performance, via providing top-k predicted intents as a set of probable directions to consider. we describe the problem definition (section 2.1), design of the intent space and discovery of underlying intents from demonstrations in an unsupervised manner (section 2.2), and fine-tuning and using the intent prediction model for acting with top-k probable intents as a flexible hint (section 2.3) in detail. ## conclusion we investigated a way to improve llm agents on downstream tasks where they possess insufficient domain knowledge. our auto-intent discovers concise intents from demonstrations and predicts multiple, semantically varied intents so that our llm policy examines the top-k intents for acting. on mind2web @xcite , a large-scale benchmark with real-website tasks, we empirically showed that our top-k intent prediction is effective for predicting correct actions and improving llm agent's performance. in addition, we performed the evaluation of our approach in an online setting on shopping tasks from the webarena benchmark @xcite , which suggests its applicability to online tasks and generalization capabilities to different domains from where it is trained.