Reliable Reasoning Beyond Natural Language

Reliable Reasoning Beyond Natural Language

The paper "Reliable Reasoning Beyond Natural Language," authored by Nasim Borazjanizadeh and Steven T. Piantadosi from UC Berkeley, explores the limitations of LLMs in performing robust and flexible reasoning. To address these limitations, the authors propose a neurosymbolic approach that integrates logical programming with LLMs. Specifically, this method prompts LLMs to translate problem statements into logical code statements and employs Prolog to execute the deductive reasoning required for solving these problems.

Introduction

The emergence of LLMs like GPT-3, PaLM, and GPT-4 has significantly advanced the field of NLP. These models have achieved human-level performance across diverse benchmarks and have demonstrated an impressive understanding of linguistic rules and patterns. However, despite their linguistic prowess, LLMs often falter in their reasoning capabilities. The autoregressive nature of transformers, which forces the models to solve problems sequentially, limits their ability to backtrack, recover from errors, and perform conditional loops. Additionally, the statistical foundation of LLMs means they struggle to generalize to novel problems, particularly those requiring reasoning and discrete processing. Even cutting-edge models like GPT-4 have short working memories, which impedes their ability to retrieve and integrate relevant information for reliable reasoning.

Proposed Approach

To overcome these limitations, the authors propose a neurosymbolic framework that combines the strengths of both neural networks and symbolic reasoning systems. The core idea is to prompt an LLM to extract and encode all relevant information from a problem statement as logical code statements in Prolog. The Prolog interpreter then handles the iterative computations required for explicit deductive reasoning.

The approach can be summarized in the following steps:

1. Prompting: A problem statement is provided to the LLM, which is prompted to perform Chain of Thought (CoT) reasoning in both text and logical code.
2. Code Generation: The LLM generates Prolog code statements that encode variable relationships and constraints.
3. Execution: The Prolog interpreter executes the generated code. If the program fails, the LLM is re-prompted until valid code is generated or a preset attempt limit is reached.

This methodology not only capitalizes on the strengths of symbolic systems like Prolog, which excel in reasoning tasks, but also leverages the natural language understanding capabilities of LLMs.

Experimental Results

The effectiveness of the proposed approach is evaluated on three different datasets: GSM8k, the Navigate task from the BIG-bench benchmark, and a newly introduced Non-Linear Reasoning (NLR) dataset.

GSM8k

The GSM8k dataset, a widely acknowledged benchmark for mathematical reasoning tasks, comprises elementary school math problems. The integration of Prolog with models like GPT-3.5 and GPT-4 significantly improved their performance on this dataset. The declarative nature of Prolog simplifies program logic, reducing the cognitive load on LLMs and eliminating the need to specify control flow or intermediate computational steps accurately.

Navigate Task

The Navigate task involves tracking an agent's location based on spatial instructions and requires updating a world model state iteratively. When tested on this task, models augmented with Prolog achieved accuracy rates exceeding 98%, demonstrating that the integration effectively mitigates arithmetic errors and enhances working memory capabilities.

Non-Linear Reasoning (NLR) Dataset

The NLR dataset consists of 55 problems designed to challenge the reasoning capabilities of LLMs beyond the linear next token prediction paradigm. The dataset includes three categories: Math Word Problems, Constraint Satisfaction Problems, and Algorithmic Instructions for updating a game model.

• Math Word Problems: These problems involve high variable entanglement, requiring iterative simplification and substitution of linear equations. The neurosymbolic approach achieved 100% accuracy in solving these problems using GPT-4.
• Constraint Satisfaction Problems: These problems encode constraints that require backtracking and exploring multiple paths to a solution. The neurosymbolic method enhanced the problem-solving performance of LLMs, particularly GPT-4, which solved all constraint satisfaction problems in the dataset.
• Algorithmic Instructions: These problems involve updating game model states based on algorithmic rules. The neurosymbolic approach improved the performance of LLMs in solving these problems, although the variability in accuracy and the number of inference attempts required was higher compared to the other problem categories.

Implications and Future Directions

The integration of Prolog with LLMs demonstrates a significant enhancement in the reasoning capabilities of these models. This neurosymbolic approach effectively addresses the limitations posed by the linear and statistical nature of LLMs. By delegating the computationally intensive deductive reasoning tasks to a symbolic system, the models can focus on natural language understanding and implicit reasoning.

The results suggest several future research directions:

1. Scalability: Expanding the NLR dataset to include a broader range of problems and reasoning patterns.
2. Error Detection: Developing methods to detect and correct logical code errors generated by LLMs.
3. Infrastructure: Enhancing the support for complex data structures in logical programming languages like Prolog.

In conclusion, the neurosymbolic approach presented in this paper offers a promising path towards creating models capable of general, reliable, and robust reasoning. The integration of symbolic reasoning systems with LLMs not only enhances their performance on existing benchmarks but also broadens their applicability to more complex reasoning tasks.