Watch Your Steps: Observable and Modular Chains of Thought (2409.15359v2)

Abstract: We propose a variant of chain of thought (CoT) prompting called Program Trace Prompting that makes explanations more observable while preserving the power, generality and flexibility of CoT. In our approach, few-shot CoT demonstrations are wrapped in a formal syntax based on Python, and each prompt: identifies and names steps; defines the input/output behavior of steps; and replaces CoT explanations of in-context examples with chains of these formalized steps on the same examples. Program Trace Prompting is applicable to many tasks, achieving strong results on the 23 diverse tasks in the BIG-Bench Hard benchmark. More importantly, by instrumenting explanations in this way, we enable new types of analysis. In particular, we identify "non-local errors" (which correspond to incorrectly learning the reasoning method illustrated in the demonstrations) as an unaddressed issue in CoT learning, and we present methods for verifying the modularity of steps in a CoT explanation.

• The paper’s main contribution is the introduction of Program Trace Prompting (PTP), a structured, Python-like syntax that renders chain-of-thought reasoning both observable and modular.
• PTP is validated across 23 tasks in the BIG-Bench Hard benchmark, maintaining accuracy and identifying non-local errors within LLM-generated explanations.
• The framework enables systematic verification of modularity through explicit input-output definitions, thereby increasing the reliability of AI reasoning processes.

Analysis of "Watch Your Steps: Observable and Modular Chains of Thought"

Cohen and Cohen's paper presents an innovative approach to chain of thought (CoT) prompting, termed Program Trace Prompting (PTP), which aims to enhance the observability, modularity, and analyzability of explanations generated by LLMs. The authors propose a structured syntax layered on CoT prompting to produce a trace of semi-formal steps with defined input-output relationships, using Python-like constructs. This initiative is motivated by, and addresses, ongoing concerns about the unfaithfulness of existing CoT prompts, which sometimes yield incorrect but superficially plausible justifications.

Key Contributions

1. Program Trace Prompting (PTP): PTP wraps few-shot CoT demonstrations in a syntax reminiscent of Python programming. This syntax allows steps within the CoT to be named, inputs and outputs to be defined, and CoT explanations to be replaced by formalized and traceable steps.
2. Broad Applicability: The authors demonstrate the utility of PTP by applying it to 23 tasks within the BIG-Bench Hard (BBH) benchmark. These tasks span both NLP and algorithmic challenges, and PTP maintains performance comparable to traditional CoT techniques.
3. Identification of Non-local Errors: By structuring CoT explanations with predetermined steps and tracing their execution, the authors identify and define non-local errors—errors occurring due to misinterpretations of the reasoning methods illustrated in demonstrations.
4. Modularity Verification: A significant contribution of PTP is its ability to evaluate the modularity of steps involved in CoT reasoning. The modularity is defined by whether a step's behavior is influenced solely by its inputs, and not extraneous context. The authors develop experimental protocols to detect non-modular steps automatically.

Experimental Insights

• Accuracy Maintenance: Experimentation shows that PTP can maintain, and sometimes improve, accuracy over CoT across diverse tasks. For instance, tasks involving structured reasoning, like algorithmic sequences, benefit from the explicit modularization.
• Structured Traces: Automatically generated traces from PTP are almost always legally parsed into specified programming steps. This structured trace allows for systematic exploration and debugging of reasoning processes.
• Error Locality and Modularity Analysis: Through PTP's structured prompting, the researchers could systematically categorize errors as local to specific steps or non-local. This categorization enhances understanding of how LLMs internalize reasoning processes and indicates that non-local errors often correlate with complex reasoning requirements.

Implications and Future Work

The PTP framework significantly advances how researchers can construct, analyze, and refine CoT prompts by leveraging a structured, programmable approach. The ability to identify non-local errors and verify modularity offers a pathway to improve explanation faithfulness, crucial for ensuring reliable and interpretable AI systems.

Further development may explore integrating executable symbolic routines within PTP or extending the methodology to support dynamic task formulations beyond static prompts. Additionally, refining the automatic generation of modular traces could streamline the deployment of LLMs in settings where explicit reasoning steps need to be tracked and verified.

The paper also indirectly opens up research inquiries into LLM's intrinsic capacity to understand and execute structured procedures, potentially intersecting with fields like program synthesis and symbolic AI.

Overall, "Watch Your Steps" provides a compelling framework that bridges CoT prompting with structured programming paradigms, offering tools to enhance transparency and robustness in AI reasoning practices.