IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction

Authors: Kaiyu He, Mian Zhang, Shuo Yan, Peilin Wu, Zhiyu Chen
Primary area: foundation or frontier models, including LLMs
Keywords: Artificial Intelligence, Reasoning, Interactive Learning Environment, Large Language Model Agent

Abstract:
While large language models (LLMs) have been thoroughly evaluated for deductive and inductive reasoning, their proficiency in abductive reasoning and holistic rule learning in interactive environments remains less explored. We introduce RULEARN, a novel benchmark specifically designed to assess the rule-learning abilities of LLM agents in interactive settings. In RULEARN, agents strategically interact with simulated environments to gather observations, discern patterns, and solve complex problems. To enhance the rule-learning capabilities for LLM agents, we propose IDEA, a novel reasoning framework that integrates the process of Induction, Deduction, and Abduction. The IDEA agent generates initial hypotheses from limited observations through abduction, devises plans to validate these hypotheses or leverages them to solve problems via deduction, and refines previous hypotheses using patterns identified from new observations through induction, dynamically establishing and applying rules that mimic human rule-learning behaviors. Our evaluation of the IDEA framework, which involves five representative LLMs, demonstrates significant improvements over the baseline. Furthermore, within this framework, our comparison with 50 human participants reveals notable discrepancies in rule-learning behaviors. LLM agents tend to generate plausible initial hypotheses but struggle to refine them through interaction. Conversely, humans, despite sometimes overlooking initial details, excel at incorporating feedback and continuously improving their hypotheses. We believe our benchmark, RULEARN, will serve as a valuable and challenging resource, and that the IDEA framework will provide crucial insights for the development of LLM agents capable of human-like rule learning in real-world scenarios. We will release our code and data upon acceptance of the paper.

Decision: reject
Reviewer scores: 5, 3, 3, 6, 6

Reviews and discussion:
<thread>
creation date:
1729928784095

thread_type:
official_review

<thread_object>
creator: reviewer_l1re

<content>
summary:
The paper introduces a benchmark for assessing the rule learning abilities of LLMs in an interactive environment. The paper also proposes a reasoning framework that does abduction, induction, and deduction in order to do well in such interactive environments. The framework outperforms the baseline, although has not reached human performance in the tasks.

strengths:
The benchmark introduced in this paper is reasonably timely, pushing towards open language description of problems that require interactive problem solving. The proposed framework of abduction, followed by induction and deduction is reasonable and showed improved performance over the baseline. Some interesting observations are made in the human studies, e.g. that humans do not do well initially and appear not to do much abduction. This can lead to interesting followup work. Overall, I think that this is useful work that is competently done and can help advance the area.

weaknesses:
Overall, the benchmark is okay for exploring interactively solving problems described in natural language. But it is not highly compelling. It is not as targetted towards human priors like the ARC challenge which targets objectness, goal-directness, number and counting, and basic geometry and topology. It is also not clearly useful like the Behaviour-1K benchmark. The size of the benchmark is also not large, so it is useful for exploring the problem with more benchmarks required for verifying the capabilities tested.

questions:
It would be helpful if the capabilities the benchmark is testing can be clearly stated, e.g. see the priors stated in the ARC challenge https://arcprize.org/arc. More insights on the human problem solving strategies used by the human participants would be useful since they appear not to do well in abduction but are more successful at the end.
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</thread>
<thread>
creation date:
1730580751807

thread_type:
official_review

<thread_object>
creator: reviewer_oa2t

<content>
summary:
This paper proposes a benchmark (RULEARN) and a framework (IDEA) to evaluate the rule-learning capabilities of LLM agents in interactive settings. The proposed framework, IDEA, integrates induction, deduction, and abduction, where the LLM agent forms initial hypotheses, devises plans to validate these hypotheses, and refines previous hypotheses. The IDEA agent is evaluated on RULEARN across three domains: the Function Operator, the Escape Room, and the Reactor. Experiments show that IDEA agents significantly outperform baselines but still lag behind human participants.

strengths:
- The paper provides a benchmark to evaluate various reasoning capabilities of LLMs in interactive settings. The proposed tasks, although mainly focused on puzzles, are interesting and serve the evaluation purpose. The results provide insights into LLMs' rule-learning capabilities in real-world scenarios.
- The experiments are comprehensive, covering a wide range of puzzle sets and five LLMs. Human experiments are also helpful. 
- The paper includes various quantitative and qualitative analyses of the experimental results. Comparisons between human and model performance provide useful insights.
- The paper is clearly written and easy to follow.

weaknesses:
- The naming of the Oracle-rule agent feels somewhat misleading to me. IMO, it is not really an oracle, as the LLM is not provided with the ground truth rule. Instead, the agent is simply solving an easier task with additional information about the rule. Therefore, this might not serve as a fair or useful upper bound, as the task is actually different. Based on L311-312, “Even if the agent could successfully learn the correct rule, applying the learned rule to solve the puzzle is non-trivial,” I assume the authors meant that the “meta-rule” is provided, but the agent still needs to determine the exact instantiation. I think this should be clarified further to avoid confusion.
- It would be interesting if the authors could include a breakdown of performance based on puzzle difficulty for different models. Difficulty could be measured using the statistics defined in Appendix A2, e.g., for the Function Operator, it could be the number of functions, etc. This could help readers understand how the performance of different models changes when varying difficulty levels.
- There are many studies evaluating LMs’ performance on text-based games, e.g. [1] and many follow-up works. In most of these settings, the LM agent needs to determine the goal and solve the game. What’s the difference between this paper and that line of work? Is the key difference that the rule is more fine-grained, or that the IDEA agent is explicitly evaluated on induction, abduction, and deduction? It would be beneficial if the authors could further discuss and clarify this.

[1] Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan, Ben Kybartas, Tavian Barnes, Emery Fine, James Moore, Ruo Yu Tao, Matthew Hausknecht, Layla El Asri, Mahmoud Adada, Wendy Tay, Adam Trischler. TextWorld: A Learning Environment for Text-based Games.

questions:
- I’m curious about the connection between this paper and active learning. The interactive action defined in IDEA is similar to adding new examples. Some studies related to this line of work, e.g. [2], are not discussed in the paper. It would be helpful to discuss these connections and differences.

[2] Wasu Top Piriyakulkij, Cassidy Langenfeld, Tuan Anh Le, Kevin Ellis. Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730637883848

thread_type:
official_review

<thread_object>
creator: reviewer_gg78

<content>
summary:
The paper proposed an interactive reasoning benchmark RULEARN, which focuses on evaluating LLM agents' rule-learning and planning abilities in an interactive environment. The benchmark contains three types of puzzles "function operator", where the agent needs to determine the parameters and element functions of a set of functions by trying different input values and observe output values; "escape room", where the agent needs to gather information in a textual environment and guess a code; "reactor" where the agent needs to figure out what a string operator do and come up with a plan to reach a specific string using the operator.

The paper also proposed a rule-learning agent, IDEA, which can perform abduction, induction, and deduction iteratively to explore an interactive environment, learn rules, and achieve goals.

strengths:
- The paper proposed a benchmark to assess LLM agents' rule-learning ability in an interactive environment, which is different from existing reasoning benchmarks where there are no interactive information-seeking actions required.
- The paper proposed an LLM agent, IDEA, which achieves higher performance than the baseline reasoning method in the proposed benchmark.
- The presentation of the paper is clear and easy to follow.

weaknesses:
While the idea of evaluating the reasoning ability of LLM agents in an interactive environment where information-seeking is necessary is interesting, there are several key drawbacks of the paper:
1. The size of the proposed benchmark is very small, with only twenty puzzles per problem. This will make the evaluation very noisy and also eliminate the possibility of any training on these tasks, which significantly limits the application of the benchmark.
2. The proposed IDEA agent needs prompt tuning for each specific problem, which limits its generalizability. Considering the proposed benchmark has limited test cases, the reviewer is worried that the good performance of the IDEA agent is a result of prompt tuning overfitting to the test problems.

questions:
1. How is the Baseline agent implemented, how is the IDEA agent different from the baseline agent? It would be good to give a prompt example that highlights the key differences between the proposed IDEA agent and the baseline agent.
2. Why use the Wilson confidence interval instead of simply the success rate as the evaluation metric, since it's less intuitive to understand the performance of different methods?
</content>
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</thread>
<thread>
creation date:
1730641030276

thread_type:
official_review

<thread_object>
creator: reviewer_fgir

<content>
summary:
This paper constructs a benchmark, named RULEARN, to evaluate the rule-learning abilities of LLM agents in interactive settings and proposes a reasoning framework, named IDEA, which integrates the process of induction, deduction, and abduction. The authors evaluate five representative LLMs.
RULEARN contains three types of tasks: The Function Operator, The Escape Room, and The Reactor.
These tasks are all about reasoning some hidden rules.
IDEA first generate a hypothesis and then generates a plan based on the hypothesis, interacts with the environment to valid the hypothesis and then refine the initial hypothesis based on observations.

strengths:
1. The authors construct an interactive environment containing three types of task, each has 20 puzzles.
2. The authors propose a three-stage framework to improve LLMs' rule-learning ability.
3. The authors conduct experiments on five popular LLMs and compare the results with human's abilities.

weaknesses:
1. The small number of questions in the benchmark may limit its applicability.
2. Improving the output of large language models (LLMs) based on the results of interactions with observations and the environment has become a common approach, as seen in methods like ReACT (https://arxiv.org/pdf/2210.03629) and Reflexion (https://arxiv.org/pdf/2303.11366). The main idea of the IDEA framework is quite similar to these works.
3. The experiment lacks some baseline comparisons, such as using search methods or observation-based reflection approaches.

questions:
What is the difference between the Abduction stage and Induction stage? It seems like these two stage are both about generate / refine a hypothesis based on observations.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730933208777

thread_type:
official_review

<thread_object>
creator: reviewer_6ubm

<content>
summary:
This works proposes RULEARN, a benchmark consists of interactive puzzles that are designed to assess the rulelearning abilities of LLM agents in interactive settings. It also proposes IDEA, an agent that dynamically establishing and applying rules that mimic human rule-learning behaviors. It has the following three steps: (1) generates initial hypotheses from limited observations through abduction, (2) devises plans to validate these hypotheses or leverages them to solve problems via deduction, (3) refines previous hypotheses using patterns identified from new observations through induction. With the novel testbed and the rule-learning framework, this paper also compares LLM agent rule-learning abilities with that of humans. Such study shows several interesting insights: (1) LLM agents continue to struggle with efficiently exploring unfamiliar environments. (2) LLMs fall short in deducting valid plans to verify current hypotheses
and guide future exploration. (3) LLMs fall short to correct previous hypotheses when they contradict new observations and are less capable of refining a hypothesis to make it more robust.

strengths:
- The RULEARN benchmark is a novel benchmark for agentic rule-learning abilities of LLMs, complementary to previous resources for assessing LLM induction abilities.
- The IDEA framework shows promises in enabling more enhanced rule-learning ability for LLM-based agents.
- The human study provides very interesting insights comparing human and LM rule induction abilities. It's especially interesting to see humans are not very good at formulating initial hypotheses.

weaknesses:
- There lacks details for how the puzzles in the benchmark are curated. Also, there's not discussion around the quality and diversity of the benchmark.
- The size of the benchmark is relatively small compared to usual benchmarks.
- The paper lacks important details of the human study, e.g., how are human participants are instructed about the task, or how are participants enforced to follow the abduction-deduction-induction framework. These details are critical for accessing the rigor of the human studies.
- Minor typo "evaluat" at L117
- Missing critical citation that thoroughly tested iterative rule refinement ability of LMs: https://arxiv.org/pdf/2310.08559

questions:
- How do you ensure the feedback from the environment is accurate in the feedback stage? How are environment feedback given (i.e., are they static pre-coded rule-based feedback, or are they dynamically provided feedback by another LM)?
- Could you be more specific about how the benchmark is created? How do you verify the quality and diversity of the benchmark?
- Have you tested the statistical significance of the human vs. agent comparison?
- Could you provide more details of how human participants are instructed about the task, and how they're trained at the beginning of the study? It'll be very helpful to provide more details of the exact human experiment setup, by providing concrete details of the procedure of the study.
- Might you have suggestions of how to improve the agentic framework for this task as future directions, based on the insights of this current study?
</content>
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</thread>
<thread>
creation date:
1731985479721

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Common concern: Puzzle size is too small (1)

comment:
### **Our puzzle size is statistically sufficient** to distinguish model performances using confidence intervals. 
- We calculated LLMs' performance confidence intervals, revealing variability that highlights each model's distinct capabilities, confirming our puzzle number suffices for performance evaluation. For example, GPT-4o's baseline success rate confidence interval is (19.21, 28.79); after applying IDEA, it changes to (30.14, 40.9), giving us 99.91% confidence that IDEA improves GPT-4o's performance. Similarly, we're 97.84% confident for LLama-70B and 99.97% for GPT-3.5. However, we reject the hypothesis that IDEA improves performance for LLama3-8b and Gemma-7B, as IDEA adds context understanding burden for low-capacity models. These results align with our paper's findings.
- Moreover, within the same setting, we're statistically confident that the performance ranking in the IDEA setting is: Human > GPT-4o ≈ LLama3-70B > GPT-3.5-Turbo > LLama3-8B > Gemma-7B. Therefore, our puzzle size suffices to distinguish model performance across settings. Adding more puzzles increases the evaluation burden without adding meaningful information.
### **The puzzle size is financially optimized** given our budget and computational constraints. 
- One trial of all puzzles (60 puzzles, expanded to 180 across 3 settings) incurs the following costs per trial:
    - 37.62$ for GPT-4o api call
    - 7.524$ for GPT-3.5 api call
    - Approximately 1 day for Llama3-70B using 8 RTX-A6000 GPUs.
    - Approximately 0.5 days for Llama3-8B and Gemma-7B, also with 8 RTX-A6000 GPUs.
    - 150\\$ for recruiting human participants. (We reduced the number of puzzles tested by humans by half, thus the actual number of puzzles per trial is 90 for human evaluation; otherwise, this cost would double to $300 per trial.)
- Over 5 trials, we spent over $1,000 and utilized 80 GPU days for open-source models. The experimental cost is higher compared to traditional QA datasets because each puzzle requires over 20 steps, with context accumulating rapidly during solving.
### **The puzzle set covers a range of difficulties** from simple to difficult.
- Since rule discovery is a broad area without a consensus on the number of task classes, we categorize the rules into three types to simulate real-life scenarios. 1) Tasks with clear systemic knowledge that support rule discovery, like math in function operator puzzles. 2) Tasks without systemic knowledge, where the rules are coarse and rely on simple intuition, such as counting attributes and clustering (e.g., escape room puzzles). 3) Tasks with no systemic knowledge and rules that need to be discovered from more fine-grained patterns.

- Within each type of puzzle, after defining the rules, we progressively populate the puzzle environment with settings ranging from easy to hard.
    - For function operator puzzles, we gradually include more parameters and functions, and more complex elementary functions like “sin(x)”, “1/x”, and “x^2”. We found that smaller models can solve puzzles with one function and up to two different parameters, while larger models struggle with puzzles containing more than three functions and five parameters. After establishing the difficulty levels, we filled the puzzle set with varying combinations of 1 to 3 functions and 1 to 6 parameters, totaling 20 different combinations.
    - In escape room puzzles, the process is similar, but the difficulty ranges from puzzles with 3 paintings to 10, and from 1 room to 3 rooms. We evenly distributed puzzles with different numbers of paintings and rooms.
    - For reactor puzzles, we defined four different reaction rules, ranging from easiest to hardest. Smaller models could solve puzzles with the simplest rule, larger models were challenged by the third synthesis rule, and only humans could solve some of the puzzles under our fourth synthesis rule. Out of 20 puzzles, there are 5 for each rule, with target strings to synthesize ranging from the shortest "ACB" to the most complex "FEADE".
- We have a clearer table in Appendix A.2 that specifies the difficulty span of our puzzle set.
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