Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought (2210.01240v4)

Abstract: LLMs have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks measure reasoning ability indirectly, by evaluating accuracy on downstream tasks such as mathematical reasoning. However, it is unclear how these models obtain the answers and whether they rely on simple heuristics rather than the generated chain-of-thought. To enable systematic exploration of the reasoning ability of LLMs, we present a new synthetic question-answering dataset called PrOntoQA, where each example is generated from a synthetic world model represented in first-order logic. This allows us to parse the generated chain-of-thought into symbolic proofs for formal analysis. Our analysis on InstructGPT and GPT-3 shows that LLMs are quite capable of making correct individual deduction steps, and so are generally capable of reasoning, even in fictional contexts. However, they have difficulty with proof planning: When multiple valid deduction steps are available, they are not able to systematically explore the different options.

The document you referenced is a paper titled "LLMs Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought" which analyzes the reasoning capabilities of LLMs like GPT-3 and InstructGPT using a novel dataset called PrOntoQA. Here’s a breakdown of the key points from the paper content:

Background and Relevance

Understanding how LLMs reason is crucial because these models are increasingly being used in applications that involve decision-making and problem-solving. Traditionally, LLMs have been evaluated based on their ability to perform various tasks, but analyzing their reasoning process can reveal whether they are genuinely reasoning or simply retrieving answers from their training data.

Comprehensive Explanation

1. Chain-of-Thought (CoT) Prompting:
   • This technique involves presenting LLMs with examples that include detailed reasoning steps, called chains-of-thought, to solve problems. It allows LLMs to use logical reasoning to arrive at answers rather than just answering questions directly.
2. PrOntoQA Dataset:
   • A synthetic question-answering dataset designed to evaluate reasoning in LLMs. Each question is based on a logical structure (ontology) and involves constructing a proof using the principles of first-order logic.
3. Reasoning Analysis:
   • The paper evaluates InstructGPT and GPT-3 models by analyzing the correctness of individual proof steps produced in the chain-of-thought. It was observed that these models can often make correct individual reasoning steps but struggle with planning the sequence of these steps.
4. Findings:
   • The models perform significantly better when the source of their reasoning matches real-world knowledge ("true" ontology) compared to fictional or false knowledge, indicating a reliance on pretrained world knowledge.
   • The paper shows that for tasks with multiple reasoning steps (hops), models frequently take wrong turns (misleading steps) and find it challenging to return to a correct reasoning path.

Pitfalls and Recommendations

• Misleading Steps: A common source of reasoning errors occurs when LLMs take valid reasoning steps that don't lead to the correct conclusion due to multiple valid pathways. These models lack robust proof-planning capabilities.
• Improvement Suggestions:
  • Employing more sophisticated reasoning strategies, potentially combining LLMs with symbolic approaches to guide them in selecting the correct proof steps.
  • Using datasets like PrOntoQA to develop training regimes that enhance models' reasoning capabilities by exposing them to structured examples that emphasize proof planning.

Conclusion

While LLMs exhibit some ability to reason, their effectiveness is often limited by their reliance on pre-existing knowledge and they are not yet capable of robust proof planning. More work is needed to enhance their reasoning abilities, particularly in contexts where the desired outcome requires deriving conclusions from novel or fictional contexts.