StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows

Summary

This paper proposes a task-solving paradigm, StateFlow, which conceptualizes complex task-solving processes as a state machine. This approach separates the control of task decomposition from sub-task solving and achieves better performance with fewer interaction turns compared to baselines on the InterCode SQL and ALFWorld benchmarks.

Rating

7

Confidence

5

Ethics flag

1

Reasons to accept

1. LLM-based complex task-solving is a significant research area, and this author tackles the problem through a controllable paradigm design, which enhances effectiveness and has the potential to advance development in this field. 2. The proposed workflow achieves higher performance in two task scenarios while utilizing fewer interaction turns, demonstrating its utility. 3. This paper is well-written and includes all necessary details. 4. The experiments convincingly demonstrate performance improvements over various baselines, including ReAct and multi-agent architectures.

Reasons to reject

1. This workflow seems effective in complex task-solving, but for simpler tasks such as translations or reading comprehension, which are foundational abilities of language models, it may not offer significant improvements. This may also explain why the performance gain is greater on the ALFWorld benchmark than on the InterCode SQL benchmark. 2. This framework is not entirely independent of human efforts. To deploy StateFlow for a specific task, we must carefully define the States, which are central to adapting it for specific tasks, along with output functions and transitions. Designing states is challenging because it initially requires observing task-solving examples. With the basic workflow in mind, we must consider all possible scenarios that might arise during the process. Moreover, as the ablation experiments show, the omission of any essential state can lower performance. This task presents challenges not only for models but also for humans. 3. This framework primarily enhances process control and does not improve reasoning or subtask solving; therefore, common errors, such as tool parameter errors, still occur. ============================ After rebuttal: Initially, I gave the paper a relatively high score due to its interesting method. If the authors can solve my questions, **I will raise the score to defend this work**. However, the authors just agree with my opinion but **do not make an effort to address my concerns**. Thus, I **decrease the score**. After another rebuttal: I have changed the score to 7 and did not raise the score due to the limitation of the paper in the method. However, I still think this work is interesting and deserves to be published.

Questions to authors

See Reasons To Reject.

Summary

The paper addresses the broad topic of LLM prompt efficiency, specifically in the context of task-oriented interactions with external tools. The paper's main contribution is to propose structuring interactions as a task-specific finite state machine, where the task is decomposed into subtasks (states) with a dedicated instruction and criteria for transitioning out of the state. This formalism is evaluated on two LLM model checkpoints (GPT-4-Turbo, GPT-3.5-Turbo) across three broad tasks: SQL and Bash tasks from the InterCode benchmark and ALFWorld, a text-adventure environment. The proposed method is compared against the InterCode benchmark methods, ReAct and Plan & Solve, reporting on success rate, error rate, and LLM usage/cost. The paper is generally easy to read and well motivated. The key innovation of the paper is the development of a state machine-inspired decomposition approach to LLM prompting. While motivated by and demonstrated on tool interaction tasks, this concept could be adapted to others where the the broader request can be decomposed into subtasks that have individual success criteria. The primary demonstrated benefit is efficiency -- fewer inference calls with fewer tokens, thus less cost, to achieve competitive or better results. The primary weakness of the the paper is in its structure. It spends a lot of time describing finite state machines from a formal basis and attempting to match elements of the StateFlow method to this. However, the paper does not motivate why this is necessary or what value there is in doing so, especially as it has to modify the original FSM formalism to account for elements specific to the LLM interaction setting. There is an interesting result here, but the desire to mathematically align with the FSM definition gets in the way. It should be enough to say that StateFlow is inspired by FSMs, provide a brief schematic, and then move on to the actual details of the experiments. The paper acknowledges that StateFlow's success is entirely dependent on the implementer to define an FSM with associated instructions and transition criteria for the specific task. In its current form, this makes it more of a strategy for structuring solutions to problems than a general method. Finally, in the appendix, there are details to enable reproducibility, including providing code in GitHub. ====== Edit: After rebuttal, the decision has changed to accept based on promised changes

Rating

7

Confidence

3

Ethics flag

1

Reasons to accept

The idea of decomposing complex requests into a state-machine structure is worth promoting.

Reasons to reject

The paper should be revised first in order to be stronger. Too much of the paper is spent reviewing the formal definition of finite state machines and attempting to adapt that to the LLM setting. This should be removed or relegated to the appendix, and replaced with content already in the appendix around the experimental details and analysis conducted.

Summary

StateFlow presents a structured approach by modeling task-solving as state machines, allowing for a systematic progression through tasks, which slightly enhances the efficiency and interpretability of LLMs. The method shows modest improvements in performance on benchmarks such as InterCode SQL and ALFWorld, suggesting potential benefits over existing models like ReAct.

Rating

6

Confidence

3

Ethics flag

1

Reasons to accept

The proposed method appears to perform well on tasks from both InterCode and ALFWorld. It also seems to reduce costs when compared with ReAct and ALFChat.

Reasons to reject

To validate the effectiveness of the method, it is recommended to conduct more experiments using other models. How does this method perform with open-source LLMs such as LLaMA3 and Mistral (e.g., 7B and 70B versions)?

Summary

The paper introduces StateFlow, a framework for enhancing task-solving processes in LLMs by using finite state machines (FSMs) to model complex workflows. The primary idea is the combination of state-driven control and dynamic interaction with external tools, thus improving the efficiency and interpretability of LLMs in multi-step problem-solving tasks. By structuring the LLM's task-solving process into distinct states and transitions, it enables better control and guidance, improving performance metrics over baseline methods like ReAct.

Rating

6

Confidence

3

Ethics flag

1

Reasons to accept

- The use of FSMs to structure task-solving processes helps in breaking down complex tasks into clearer, more manageable steps, which enhances the interpretability and "debuggability" of LLMs in complex scenarios. - The paper presents empirical evidence where StateFlow outperforms existing methods like ReAct in specific benchmarks with lower cost, indicating its effectiveness in controlled scenarios. - The potential ability to integrate StateFlow with other iterative and refining methods.

Reasons to reject

- For me, the main concern of this paper is that the core idea of applying state machines to manage LLM workflows seems to repackage a well-established concept without providing transformative novelty. In the context of LLMs, while the paper presents this as a novel methodology, lots of agent already consider the similar ideas, albeit without explicitly claiming the FSM label. - It seems that the demonstrated benefits of StateFlow are primarily shown in "controlled" benchmarks, which might not fully convince of its broad applicability or superiority in diverse real-world scenarios. - I can resonate with the authors' choice of baselines, as there are indeed many agent-based methods that are complex and difficult to implement. However, I feel that the choice of baselines might be slightly weak. I would have liked to see comparisons with other methods, many of which are referenced in the related work section. This would help to contextualize StateFlow's performance and innovative value more robustly within the current landscape.

Questions to authors

- It seems that the states required to be pre-defined. Considering that human cognitive processes are not strictly confined to finite states, can you provide more insights on how StateFlow performs in highly dynamic environments where the tasks or the nature of the interactions change over time? Specifically, how does StateFlow adapt when faced with tasks that have evolving goals or when unexpected changes occur in the environment? - Any thoughts or ideas on some more complex tasks, e.g., SWE-bench or real-world Web interaction?