Faithful Logic-Aided Reasoning and Exploration: A New Approach for Question Answering

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Large Language Models (LLMs) are revolutionizing the field of Question Answering (QA), but they often struggle with complex reasoning tasks. While prompting techniques like Chain-of-Thought (CoT) aim to improve reasoning, they can lead to unfaithful outputs, lacking consistency with the intermediate reasoning steps. Alternatively, neuro-symbolic methods, such as Faithful CoT (F-CoT), combine LLMs with external symbolic solvers for high faithfulness, but they often require code generation and struggle with ambiguous or difficult-to-formalize queries.

A new research paper, published at ICLR 2025, presents Faithful Logic-Aided Reasoning and Exploration (FLARE), an interpretable approach that addresses these limitations. FLARE leverages the strengths of LLMs for planning and fuzzy reasoning while introducing a novel logic-based search mechanism for problem exploration.

Here’s how FLARE works:

1. Plan Generation: The LLM analyzes the query and generates a plan, outlining key concepts and steps for formalizing the question. For example, to answer the question “Do all parts of the aloe vera plant taste good?”, FLARE might suggest a plan that involves defining “aloe vera,” “plant,” and “taste good.”
2. Code Generation: The LLM uses the plan to generate a logic programming code (Prolog) that formalizes the query into facts and relations. For example, the code might include facts like plant(aloe_vera, medicinal, cosmetic) and relations like combined(aloe_vera, petroleum_derived) :- plant(aloe_vera,_,_), product(petroleum_derived, _), ingredient(emulsifier,_,_).
3. Simulated Search: The LLM simulates the execution of the Prolog code, performing an exhaustive multi-hop search through the defined space. This simulation allows FLARE to understand the reasoning process and identify potential hallucinations or inconsistencies.

FLARE’s key advantages:

• Interpretability: FLARE provides a transparent explanation of the reasoning process, allowing researchers to understand how the model arrived at its answer.
• Faithfulness: FLARE measures the faithfulness of the reasoning process by comparing the simulated search with actual Prolog execution. This helps identify instances of hallucination or sub-optimal reasoning.
• Soft-Formalization: FLARE uses Prolog code for soft-formalization, allowing for flexible reasoning and handling of ambiguous queries.
• State-of-the-art performance: FLARE outperforms CoT and F-CoT on diverse reasoning benchmarks, demonstrating its effectiveness across various tasks.

FLARE’s impact:

FLARE provides a promising new paradigm for complex reasoning in QA. By combining the strengths of LLMs with a logic-based search approach, FLARE achieves high accuracy and interpretability. This research sheds light on the importance of reasoning faithfulness in QA, paving the way for more robust and reliable LLM-based reasoning systems.

Example:

Consider the question “A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?”

FLARE might generate the following:

1. Plan: Define “blue fiber,” “white fiber,” and “robe.” Calculate half the amount of blue fiber for white fiber. Add both amounts to get the total.
2. Code: fact(blue_fiber, 2). fact(white_fiber, blue_fiber/2). query:- fact(blue_fiber, X), fact(white_fiber, Y), total(X+Y, Z), write(Z)..
3. Simulated Search: FLARE simulates the execution of the Prolog code, determining the total number of bolts as 3.

This example showcases FLARE’s ability to systematically break down complex reasoning problems into manageable steps, providing a clear and interpretable solution.

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