Proof of Thought (PoT) Paper: Summary and Implications

NEW -> AI Generated PoDcast (cool!) using Google's Notebook LM.

This is a discussion of the paper: "Proof of Thought: Neurosymbolic Program Synthesis for Robust & Interpretable Reasoning": https://lnkd.in/gtRyBGzN done principally by Debargha Ganguly who interned at Microsoft Research India mentored by Srinivasan Iyengar and with Prof. Vipin Chaudhary .

This work was inspired by real world challenges we encountered in:

• Video-based situational awareness in Health and Safety (HSE), thanks to our work with clients like Petronas (h/t to Farid Faliq Shukri , Liang Kim Meng and their teams) and
• Reliability and Capital Projects with McKinsey (h/t Vivek Lath Abdul Qavi Mohammed Sasivarman Sellon Sunalini Sinha Rezon Jovian Azam Mohammad and colleagues), and with inputs from a broader set of colleagues in Microsoft ( Astha Agarwal , Pratima R. , Guruprasad Thunuguntla , kishan choudhury , Niyati Jain , Jaideep Singh Sachdev , Keny Patel Sourav Sahu Sparshi Jain Varsha Parthasarathy Hariprasad Bobbala Tanuja Ganu (MSR) Akshay Nambi (MSR), Amit Sharma (MSR), Sriram Rajamani (MSR) Sameer Segal (MSR), Venkat Padmanabhan (MSR) Swarup Joshi , Srikant Kadambi Jim Bullock Monika Bagchi CK LOH Tern-Yun Soon Justin Yeap Stacey Lusk and others).

Though this was inspired by the Energy Industry, we believe it has value in other cross-industry applications seeking more dependable outputs from LLMs and responsible AI considerations.

In keeping up with times, I used ChatGPT 4o (with my edits) to summarize and discuss a few aspects of this paper. Enjoy!

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Short summary:

Problem: LLM outputs need to be more reliable and dependable.

Approach: Transform LLM outputs into an intermediate logic that can be checked by theorem provers.

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Key Takeaways (h/t ChatGPT 4o, with author's edits):

Key takeaways from the paper Proof of Thought: Neurosymbolic Program Synthesis Allows Robust and Interpretable Reasoning (PoT) summarized in layman’s terms, along with their implications:

1. Improved AI Reasoning: The PoT framework allows AI models to generate more reliable and interpretable reasoning by transforming AI’s outputs into a form of logic that can be checked by a program. This ensures that the AI’s reasoning is clear and provable.
2. Bridging Human Thought and Logic: PoT uses a special language (JSON-based DSL) that is easy for humans to understand but also precise enough for formal logic checks. It acts as a middle ground between human reasoning and machine logic.
3. Human-in-the-Loop: PoT emphasizes human oversight in AI decision-making. The system is designed to allow human experts to step in and verify or correct the AI’s reasoning.
4. Tackling Complex Problems: The framework is particularly effective in solving complex problems that require multiple steps of reasoning, like answering tricky questions or finding hazards in images.
5. Formal Verification: The core strength of PoT is the use of theorem proving, which ensures that AI’s conclusions are mathematically accurate, based on true premises.
6. Reducing Errors: By breaking down complex reasoning into smaller, provable steps, PoT reduces the chances of AI making logic errors or jumping to conclusions.
7. Customizable and Scalable: PoT is designed to be flexible and scalable, meaning it can be adapted to a wide range of tasks and industries. It can also grow more sophisticated over time by integrating more rules and logic.
8. Interpretable Reasoning: One major advantage is that every decision or inference made by the AI is traceable. You can see exactly how the AI arrived at its conclusion, making it more transparent.
9. Benchmark Performance: The PoT framework was tested on tasks like the StrategyQA dataset (challenging questions) and Reddit-OSHA (safety hazard identification). It performed well in generating logical reasoning paths that could be verified.
10. Feedback Loop: PoT uses a feedback loop to fix errors in its reasoning process. If the AI makes a mistake, it tries again, learning from its errors.

• Implication: This iterative improvement leads to more accurate AI over time, making it robust in unpredictable or difficult scenarios.

In essence, the PoT framework allows AI systems to explain their decisions in ways humans can understand, while also ensuring their reasoning is logically sound and verifiable. This opens up possibilities for using AI in critical areas where trust and accuracy are essential.

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Compare PoT (Proof of Thought) vs CoT (Chain of Thought), Tree of Thought (ToT), Graph of Thoughts (GoT)

(h/t ChatGPT 4o with edits)

• PoT: Ensures provable correctness using formal logic, ideal for high-stakes reasoning.
• CoT: Simple, step-by-step reasoning, good for tasks that require logical progression.
• ToT: Branching approach to explore multiple solutions, useful in planning or problem-solving.
• GoT: Graph-based reasoning for complex, interconnected problems with cyclic thoughts.

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What is the potential of PoT in Responsible AI to combat LLM Hallucinations?

(h/t ChatGPT 4o with edits)

The Proof of Thought (PoT) framework can potentially help address hallucinations in LLM outputs, especially in situations where LLMs generate confidently incorrect (hallucinated) responses. Here's how and why:

1. Formal Verification of Reasoning:

• How it helps: One of the key strengths of PoT is that it turns LLM outputs into formal logic statements that can be verified by a theorem prover. This means that PoT doesn't just rely on the patterns LLMs use to generate text—it actively checks if the reasoning behind the answer is logically sound. If the reasoning is incorrect or if there are contradictions in the logic, the theorem prover will flag the output as false or provide a counterexample.
• Why it's important: Hallucinations occur when LLMs produce plausible-sounding but false information. By introducing formal logic verification, PoT ensures that only provably true statements are accepted, significantly reducing the chances of hallucinations.

2. Transparency in the Reasoning Process:

• How it helps: In PoT, every step of the reasoning process is transparent and traceable. If an LLM generates a questionable or incorrect response, you can trace the reasoning chain and identify exactly where the mistake occurred.
• Why it's important: One major issue with LLM hallucinations is that it's often difficult to understand why the model made a mistake because the reasoning process is not explicit. PoT makes the reasoning process clear and interpretable, allowing users to catch and fix errors more easily.

3. Distinguishing Factual from Inferential Knowledge:

• How it helps: PoT introduces a system that clearly separates factual knowledge (information from the knowledge base) from inferential knowledge (logical conclusions drawn from facts). By doing this, it ensures that facts are used correctly in reasoning processes and that inferences are based on sound logic.
• Why it's important: Hallucinations often occur when LLMs mix facts with incorrect inferences. PoT ensures that facts are treated properly and that any inferences are logically verified, which can help prevent LLMs from making incorrect jumps or conclusions.

4. Human-in-the-Loop Oversight:

• How it helps: PoT is designed for human oversight. After generating a reasoning chain, humans can step in to inspect, verify, or correct the logical flow. This oversight can act as a safety net to catch any hallucinations before they make it into the final output.
• Why it's important: LLMs are known to make confident but incorrect statements, especially in areas where they lack complete knowledge. With PoT, humans can intervene and review the logical steps, helping to prevent confidently incorrect outputs from going unchecked.

5. Feedback Loop for Error Correction:

• How it helps: PoT incorporates a feedback loop to catch and fix errors in reasoning. If the model produces a flawed or hallucinated output, it tries again to correct the mistake based on the logical verification. This iterative process helps improve the reliability of the model's outputs.
• Why it's important: LLMs may hallucinate information because they lack mechanisms for self-correction after making mistakes. PoT’s feedback loop provides a way for the model to identify and correct these mistakes, reducing the likelihood of repeated hallucinations.

6. Application in High-Stakes Domains:

• How it helps: In domains like healthcare, legal analysis, or safety verification, where hallucinations can have serious consequences, PoT's guaranteed reasoning can ensure that only factually and logically correct outputs are produced.
• Why it's important: LLM hallucinations can be particularly dangerous in high-stakes fields where incorrect information can lead to harmful decisions. PoT's verifiable logic-based approach helps prevent such mistakes, ensuring that outputs are not only plausible but also provably true.

Key Differences from LLM Alone:

• LLM (e.g., ChatGPT): LLMs generate responses based on patterns learned from vast amounts of data. While they can produce highly accurate and coherent responses, they lack the ability to validate the factual accuracy of their outputs. This leads to hallucinations, where the model generates confident but incorrect information.
• PoT: In contrast, PoT takes LLM-generated outputs and subjects them to rigorous logical checks. This means that hallucinations (plausible-sounding but incorrect outputs) are less likely to occur because the output is verified against formal logic rules.

Example of How PoT Can Prevent Hallucinations:

Imagine an LLM generates the response, "Aristotle used a laptop to write his philosophy." Normally, the LLM might sound confident, but this is clearly false. PoT would:

• Break this response into logical components (e.g., Aristotle lived in 384–322 BC, laptops were invented in the 20th century).
• Use a theorem prover to verify whether the logic holds (does the timeline of Aristotle align with the existence of laptops?).
• Flag the output as incorrect because it would detect that Aristotle could not have used a laptop based on historical facts.
• Either provide a counterexample or prevent the hallucinated response from being used.

Limitations of PoT in Preventing Hallucinations:

While PoT significantly reduces the likelihood of hallucinations by introducing formal logical checks, it is not a complete solution to every problem:

• Contextual knowledge gaps: If the underlying knowledge base lacks critical information, the PoT framework could still make incorrect conclusions.
• False positives: As seen in some benchmarks, PoT can still produce false positives when it overestimates connections between facts. This can lead to logical inferences that aren't strictly valid but are assumed to be true.
• Generalization challenges: In situations where PoT needs to handle ambiguous or unstructured information, it may struggle to apply its formal logic rules effectively, leading to either conservative responses or incorrect verification.

Conclusion:

The PoT framework provides a promising approach to reducing hallucinations by making LLM outputs verifiable, transparent, and traceable. It ensures that the reasoning behind AI-generated answers can be logically checked, thus preventing many confidently incorrect statements from being accepted as true. While it's not foolproof, especially in cases of knowledge gaps or complex reasoning, it marks a significant improvement in tackling the problem of hallucinations in AI-generated content.

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Guarantees, Assume-Guarantee Reasoning, and Limitations

Proof Of Thought (PoT) provides guarantees on reasoning correctness through its integration with formal verification, leveraging the principles of the assume-guarantee paradigm.

This paradigm, widely used in formal verification of complex systems, allows decomposition of the verification task into smaller, manageable parts. In the context of PoT, the "assumptions" represent the knowledge base (KB) and rule specifications provided to humans via the "Thought Program", while the "guarantees" refer to the correctness of the reasoning chain, as validated by the theorem prover.

Specifically, PoT guarantees that if the KB and rules accurately reflect the domain knowledge and intended reasoning logic, then any conclusion derived by the system is logically sound.

This is a crucial distinction: PoT does not guarantee the absolute correctness of the answers themselves, but rather the validity of the reasoning process based on the provided inputs. Even if the final answer is incorrect, PoT guarantees that the path taken to reach that answer is logically consistent with the established KB and rules.

This separates the question of factual accuracy from the question of reasoning validity. However, several limitations exist.

• Firstly, the accuracy of the final answer is conditionally dependent on the human-provided KB and rules. If these are incomplete, inaccurate, or contain inconsistencies, the system may produce logically correct but factually incorrect answers.
• This requires human oversight in the formulation of KB and rules, particularly in novel or complex domains.
• Multi-hop reasoning, as required in the StrategyQA dataset, amplifies this challenge. While PoT can audit the reasoning chain, and techniques like CoT-SC, GoT, and ToT can explore different reasoning paths, the initial program generation in PoT might not capture all necessary facts or rules in the first attempt.
• We currently employ feedback loops with LLM-based techniques (like CoT-SC) to address this, but further refinement is needed to ensure completeness and accuracy of the generated programs.

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Overall Balanced Summary;

(h/t ChatGPT 4o with edits)

The Proof of Thought (PoT) paper introduces a neurosymbolic framework that enhances Large Language Model (LLM) reasoning by integrating formal logic verification. PoT translates LLM outputs into First Order Logic (FOL), and a Domain Specific Language (DSL) ensuring that the reasoning can be rigorously verified by theorem provers. This approach improves transparency, reliability, and interpretability, addressing key challenges in fields like healthcare, legal reasoning, and safety-critical applications. While early evals have room for improvement on false positive rates, this is an overall high potential approach. The validation is also conditionally dependent upon the human provided knowledge base and rules.

Potential:

• Verifiable Reasoning: Ensures logically sound outputs through formal proofs, reducing errors and improving trust in AI decisions.
• Human-Readable Logic: Converts reasoning into interpretable steps, making AI decision-making transparent and traceable.
• Applicability in High-Stakes Domains: Suited for sectors that require high accountability and accuracy, like healthcare, law, and engineering.
• Error Detection: Provides counterexamples when reasoning fails, allowing for systematic correction of errors.
• Human-in-the-Loop: Facilitates oversight by enabling users to verify and adjust AI's reasoning process.

In summary, PoT offers a robust, verifiable method for improving LLM reasoning in structured and critical applications.