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AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents

Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, Honglak Lee

arXiv:2403.08978v2cs.CLcs.LG

TL;DR

LLM agents can struggle in unfamiliar domains, and demonstrations may be constrained by context length, prompt sensitivity, and complex reasoning. AutoGuide generates concise, context-aware guidelines from offline experiences and retrieves relevant guidance at test time. The paper reports that it outperforms competitive baselines in complex benchmark environments.

  • Problem

    In unfamiliar domains such as web navigation, LLM agents may lack sufficient prior knowledge, and providing all demonstrations can be limited by context length, prompt sensitivity, and complex reasoning.

    Source: §1 · p. 1

  • Method

    AutoGuide contrasts offline trajectories to generate concise natural-language guidelines with explicit applicable contexts, then retrieves relevant guidelines for the agent’s current context at test time.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    AutoGuide reports the highest success rates against competitive baselines in complex sequential decision-making benchmarks, including real-world web domains.

    Source: §1 · p. 2

  • Takeaways & Limitations

    AutoGuide provides context-relevant, concise guidance from offline experiences for LLM agents’ decision-making in the evaluated benchmark domains.

    Source: §1 · p. 2

    Performance depends on offline-experience diversity, and the paper reports no standardized method for quantifying generated-context and selected-guideline quality.

    Source: A Limitation and Broader Impacts · p. 13

Abstract

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Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perform well in unfamiliar domains like web navigation, where they lack sufficient knowledge, has proven to be difficult with the demonstration-based in-context learning paradigm. In this paper, we introduce a novel framework, called AutoGuide, which addresses this limitation by automatically generating context-aware guidelines from offline experiences. Importantly, each context-aware guideline is expressed in concise natural language and follows a conditional structure, clearly describing the context where it is applicable. As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process, overcoming the limitations of the conventional demonstration-based learning paradigm. Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains, including real-world web navigation.

1 Introduction

LLM agents can struggle in unfamiliar domains, and providing all demonstrations may be limited by context length, prompt sensitivity, and complex reasoning. AutoGuide extracts concise, context-aware guidelines from offline experiences and reports the highest success rates against competitive baselines in complex sequential decision-making benchmarks.

1 Introduction

  • LLM agents may lack sufficient prior knowledge in unfamiliar domains such as web navigation, while diverse and dynamic content can make success difficult.Providing all available experiences as demonstrations can also be limited by context length, prompt sensitivity, and complex reasoning.Source: §1 · p. 1
  • Offline experiences can convey desirable and undesirable policies, but extracting their implicit information into actionable guidance is a key challenge.AutoGuide addresses this by generating concise natural-language guidelines that identify their applicable contexts.Source: §1 · pp. 1–2
  • AutoGuide reports the highest success rates against competitive baselines in complex sequential decision-making benchmark environments.Its contributions also describe evaluation across interactive domains, including real-world web navigation, and improved performance on out-of-domain tasks.Source: §1 · p. 2
  • AutoGuide uses context identification and guideline extraction modules to generate context-aware guidelines from offline experiences.The paper also studies guideline applicability and generalization across out-of-domain tasks.Source: §1 · p. 2

2 Related Work

The paper relates AutoGuide to agent learning from feedback and to natural-language guidance. It distinguishes its use of cross-task offline experience and contextual guideline retrieval from per-episode reflection and non-contextual guidance.

Self-reflection from past experiences

  • Self-feedback uses immediate feedback within an episode, whereas AutoGuide draws on multiple offline training tasks for complementary inter-task knowledge.The paper says the approaches can be used together.Source: §2 · p. 3

Leveraging natural language guidance

  • Unlike dynamically generated guidance based on human demonstrations, AutoGuide compares offline trajectories; unlike ExpeL, it retrieves guidelines relevant to the current state instead of providing all guidelines.The paper reports that this contextual selection substantially improves over ExpeL’s non-contextual approach.Source: §2 · p. 3
  • Prior work also explores learning from manuals and automatically generated chain-of-thought guidance for agent actions.The paper situates these methods within natural-language guidance for efficient action.Source: §2 · p. 3

3 AUTOGUIDE: Principled Method Based on Context-Aware Guidelines

AutoGuide distills offline trajectories into concise guidelines linked to the contexts where they apply. At test time, it identifies the current context, retrieves relevant guidelines, and incorporates them into the agent’s action prompt.

3 AUTOGUIDE: Principled Method Based on Context-Aware Guidelines

  • AutoGuide’s method uses offline experiences to construct context-aware guidelines and apply them to guide action generation at test time.The stated objective is to distill offline knowledge into natural language that helps maximize expected test-time return.Source: §3 · p. 3, §3.1 · p. 3

3.1 Problem Statement

  • The objective is to distill knowledge from offline trajectories into natural language that helps maximize expected return during testing.The training data consists of trajectories of observations, actions, and rewards.Source: §3.1 · p. 3

3.2 Extraction of Context-Aware Guidelines

  • AutoGuide contrasts offline trajectories with different returns, finds where their actions first diverge, summarizes the shared prefix as context, and generates a guideline for that context.It organizes guidelines by context and uses an LLM to merge differently worded contexts describing the same situation, reducing redundancy.Source: §3.2 · p. 4

3.3 Applying Context-Aware Guidelines at Test Time

  • At each test timestep, AutoGuide identifies the trajectory’s current context, selects relevant guidelines, and incorporates both into the action-generation prompt.When a context has more than k possible guidelines, a selection module prompts an LLM to choose top-k for the current trajectory.Source: §3.3 · p. 5
  • Context-aware guidelines provide relevant assistance for a context, are concise enough for prompts, and operate at individual decision points rather than whole trajectories.The paper notes that omitting a guideline’s applicable context can confuse decision-making, while a single incorrect action can lead to complete failure.Source: §3.3 · p. 5

4 Evaluation

Evaluation tests AUTOGUIDE across sequential decision-making benchmarks and examines its components, in-context examples, and transfer. The reported results favor context-aware guidelines over baselines, with additional gains when combined with test-time self-feedback.

4.1.1 Sequential Decision-Making Benchmark Domains

4.1.2 Baselines

  • ReAct uses no offline experiences, ExpeL supplies all extracted guidelines, and Reflexion converts environmental feedback into text for a later trial.Source: §4.1.2 · p. 6

4.1.3 Implementation

  • Offline data comes from ReAct, Reflexion, or human demonstrations; GPT-4-turbo extracts guidelines, which are tested on non-overlapping tasks.Source: §4.1.3 · p. 6

4.2 Main Results

  • AUTOGUIDE achieves the highest test rewards and success rates on ALFWorld and WebShop, and the highest WebArena success rate, with a 47.1% enhancement versus ExpeL’s 21.8%.Source: Table 1 · p. 7, §4.2 · pp. 7–8

4.3 Analyses of AUTOGUIDE

  • ReAct’s raw demonstrations plateau and often exceed token limits beyond six shots, whereas AUTOGUIDE beats the no-guideline baseline at every tested k, peaking at k=3.Source: Table 3 · p. 8, Table 4 · p. 8, §4.3 · p. 9

4.2 Main Results

  • GPT-4V AUTOGUIDE significantly improves performance across three real-world multimodal domains, while AUTOGUIDE with Reflexion performs best on WebShop and ALFWorld.Source: §4.2 · p. 8, Table 2 · p. 8

4.3 Analyses of AUTOGUIDE

  • Context-only and guideline-only variants each improve WebShop performance over ReAct, but the full system performs better, supporting integration of both components.Source: Table 6 · p. 9, §4.3 · p. 9
  • WebShop guidelines transferred to WebArena-Shopping improve success rates over ReAct after a GPT-4-Turbo grounding module aligns them to WebArena observations.Source: Table 5 · p. 9, §4.3 · p. 9

5 Conclusion

AutoGuide uses offline experiences to create context-aware guidelines for LLM agents, supplying guidance relevant to each encountered context. The paper reports that it outperforms strong baselines in decision-making benchmarks.

5 Conclusion

  • AutoGuide exploits offline domain knowledge by contrasting trajectories to generate context-aware guidelines intended to prevent failures.At inference, it supplies guidelines pertinent to each context an agent encounters.Source: §5 · p. 9
  • The framework aims to improve decision-making with pre-trained LLMs by incorporating generated guidelines into agent prompts.Source: §5 · p. 9
  • AutoGuide outperforms strong baselines by a large margin and achieves outstanding performance in decision-making benchmarks.Source: §5 · p. 10

A Limitation and Broader Impacts

AutoGuide's performance depends on the diversity of offline experiences, and the paper identifies the lack of a standardized way to quantify generated contexts and selected guidelines. The authors also note potential societal risks from biased datasets and agent misuse.

A Limitation and Broader Impacts

  • AutoGuide's performance depends on the diversity of offline experiences.The authors identify automatically collecting diverse experiences through continual learning as a direction for improvement.Source: A Limitation and Broader Impacts · p. 13
  • No standardized method currently quantifies generated-context and selected-guideline quality beyond applying guidelines to ReAct and measuring test-time performance.Source: A Limitation and Broader Impacts · p. 13
  • Biased offline datasets may lead to suboptimal decisions, and autonomous agents may be misused for malicious applications.Proposed mitigations include dataset diversification, ethical oversight, transparency, accountability, stakeholder engagement, and security measures.Source: A Limitation and Broader Impacts · p. 13

B Evaluation Details

The ALFWorld evaluation describes the agent's available interactions, its test-task coverage, and its action limit. Agents can also use think actions for planning and reflection without changing the environment.

B.1.1 Environment details

  • ALFWorld agents can navigate to receptacles, take or place objects, open, close, or use receptacles, and clean, heat, or cool objects.Source: §B.1.1 · p. 13
  • A think action supports planning and reflection without changing the environment; after each action, the agent receives an observation describing view changes.Source: §B.1.1 · p. 13
  • The evaluation uses 134 unseen test tasks across six task categories, with a maximum of 50 actions per task.Source: §B.1.1 · p. 13

B.1.2 Baseline and Models

The appendix describes ALFWorld baseline and guideline-generation settings, then outlines WebShop interactions and its trajectory inputs. In WebShop, agents search or click, and use prior observation-action pairs to generate the next action.

B.1.2 Baseline and Models

  • ALFWorld baselines use two in-context examples per task category; Reflexion appends reward-based feedback after failures, while AutoGuide plus Reflexion concatenates feedback across episodes.The ReAct implementation uses gpt-3.5-turbo-instruct because Text-Davinci-002 is no longer available.Source: §B.1.2 · p. 13
  • Guidelines are extracted from the first 100 ALFWorld training tasks collected with ReAct plus Reflexion, using two-shot demonstrations for context identification in each of six categories.The appendix says episode-length and n-shot hyperparameters follow ReAct's setup.Source: §B.1.3 · p. 14, Table 7 · p. 14

B.2.1 Environment details

  • In WebShop, the agent searches or clicks, and receives a reward for how well its purchase matches the target product.The stated match dimensions are type, price, buying options, and attributes; the agent uses prior observation-action pairs to generate its next action.Source: §B.2.1 · p. 14

B.2.2 Baseline and Models

The WebShop experiments compare AutoGuide with ReAct, ExpeL, and Reflexion using specified prompting and evaluation setups. AutoGuide guidelines are also tested with Reflexion feedback.

B.2.2 Baseline and Models

  • The comparison uses one-shot ReAct, ExpeL with appendix guidelines only, and Reflexion with rewritten prompts and GPT-4-1106-preview-generated episode reflections.ExpeL's in-context example selection is excluded; Reflexion reflections are appended after AutoGuide guidelines when the methods are combined.Source: §B.2.2 · p. 14
  • For the AutoGuide-plus-Reflexion setup, guidelines come from 50 sampled WebShop training tasks, and GPT-3.5 selects up to two at test time.Context identification uses a one-shot prompt.Source: §B.2.3 · p. 15
  • WebShop hyperparameters are listed in Table 8, with episode length, results per page, and n-shots following ReAct's setup.Source: Table 8 · p. 15

B.3.1 Environment details

WebArena Reddit tasks use realistic website content represented as accessibility trees. The experiments specify the observations, action limits, and available training demonstrations.

B.3.1 Environment details

  • WebArena represents webpage content as an accessibility tree, and the experiments focus on Reddit, which simulates real Reddit users, forums, and posts.Source: §B.3.1 · p. 15
  • At each timestep, observations include open tabs, the focused page's accessibility tree, and its URL; agents receive only the current observation plus up to five past actions.Source: §B.3.1 · p. 15
  • The available actions include navigation, clicking, typing, key presses, scrolling, and going back.Source: §B.3.1 · p. 15
  • Tasks allow at most 20 actions; because WebArena supplies 19 Reddit demonstrations and no training tasks, those demonstrations train the method before evaluation on the remaining 87 tasks.Source: §B.3.1 · p. 15

B.3.2 Baseline and Models

The appendix describes WebArena baselines, environment details, and a modified visual baseline for real-world website tasks.

B.3.2 Baseline and Models

  • The WebArena baseline uses two-shot ReAct with GPT-4-preview-1106, while ExpeL is implemented by the authors on the same training tasks as AutoGuide.Table 9 lists WebArena experiment hyperparameters; the number of shots follows ReAct's setup.Source: §B.3.2 · p. 15, Table 9 · p. 16

B.4.1 Environment details

  • Real-world website evaluation covers GitHub, Google Flights, and Coursera with 30, 20, and 20 test tasks, respectively, and human experts assess completed tasks because the sites change dynamically.Source: §B.4.1 · p. 16

B.4.2 Baseline and Models

  • The Visual WebArena SoM baseline is run in its official codebase with invisible bounding boxes filtered out and list elements added as interactable elements.Source: §B.4.2 · p. 17

B.4.3 Implementation details of AUTOGUIDE

The appendix specifies AutoGuide's multi-modal real-world setup and provides domain prompts and examples for context identification, guideline extraction, matching, and selection.

B.4.3 Implementation details of AUTOGUIDE

  • For real-world website tasks, six training tasks provide human demonstrations and SoM trajectories, which are converted into text guidelines using GPT-4-vision-preview.The six tasks cover GitHub, Google Travel, and Coursera; context identification uses a three-shot prompt.Source: §B.4.3 · p. 17

C.1 Context Identification

C.3 Context Matching

C.4 Guideline Selection

D Example Context-Aware Guidelines

C.2 Guideline Extraction

  • The ALFWorld extraction prompt compares desired and undesired trajectories, identifies their first differing action, and produces a concise guideline conditional on status.Source: D Example Context-Aware Guidelines · p. 21

1. Claims

The authors report that the paper’s claims reflect its contribution and empirical evaluation, that limitations are discussed in Appendix A, and that experimental details are provided for reproducibility. They also note that results omit variance.

1. Claims

  • The authors say the introduction accurately presents the contribution—extracting context-aware guidelines from offline data—and supports effectiveness through empirical evaluation.Source: §1 · p. 25
  • Potential limitations of AutoGuide are outlined in Appendix A.Source: §1 · p. 25
  • Reproduction details include model versions, hyperparameters, training-data counts, and prompt templates.Source: §1 · p. 26
  • The authors intend to release the source code upon acceptance.Source: §1 · p. 27
  • Average rewards and success rates are reported across test tasks, but variance is not included.Source: §1 · p. 27
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