GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation (2410.08475v3)

Abstract: Existing approaches based on context prompting or reinforcement learning (RL) to improve the reasoning capacities of LLMs depend on the LLMs' internal knowledge to produce reliable Chain-Of-Thought (CoT). However, no matter the size of LLMs, certain problems cannot be resolved in a single forward pass. Meanwhile, agent-based reasoning systems require access to a comprehensive nonparametric knowledge base, which is often costly or not feasible for use in scientific and niche domains. We present Graph Inspired Veracity Extrapolation (GIVE), a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input. GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak). Extensive experiments demonstrated the following benefits of our framework: (1) GIVE boosts the performance of LLMs across various sizes. (2) In some scenarios, GIVE allows smaller LLMs to surpass larger, more sophisticated ones in scientific tasks (GPT3.5T + GIVE > GPT4). (3) GIVE is effective on scientific and open-domain assessments. (4) GIVE is a training-free method that enables LLMs to tackle new problems that extend beyond their training data (up to 43.5% -> 88.2%} accuracy improvement). (5) GIVE allows LLM agents to reason using both restricted (very small) and noisy (very large) knowledge sources, accommodating knowledge graphs (KG) ranging from 135 to more than 840k nodes. (6) The reasoning process involved in GIVE is fully interpretable.

• The paper introduces the GIVE framework, integrating sparse knowledge graphs with LLM internal memory to enable structured veracity extrapolation.
• It employs techniques like entity grouping, narrative induction, and counterfactual reasoning to progressively refine answer generation.
• Empirical results show GIVE outperforming state-of-the-art methods in biomedical and commonsense QA using sparse knowledge sources like UMLS and ConceptNet.

Structured Reasoning with Minimal Knowledge Graphs: The GIVE Framework

The paper "GIVE: Structured Reasoning with Knowledge Graph Inspired Veracity Extrapolation" presents an innovative approach to reasoning in LLMs by leveraging both parametric and non-parametric memories. The proposed GIVE framework addresses the challenges faced by existing retrieval-based reasoning methods, particularly in domains where constructing comprehensive non-parametric knowledge sources is impractical.

Overview and Methodology

GIVE introduces a sophisticated reasoning structure that integrates sparse external knowledge from Knowledge Graphs (KGs) and the internal memory of LLMs. The approach emphasizes a more methodical reasoning process, contrasting with direct answer retrieval. It does so by prompting LLMs to break down queries into key concepts and attributes, constructing entity groups, and developing an augmented understanding of relationships both factual and extrapolated.

Key steps in GIVE's framework include:

1. Entity Group Construction: By identifying and grouping semantically related entities, GIVE populates a sparse KG with additional connections, termed as "silver edges." These augmentations are derived from both factual linkages and the inherent knowledge of the LLM.
2. Narrative Induction: The framework induces relationships within and between these groups, using a diverse set of potential relations identified through a process coined "veracity extrapolation."
3. Counterfactual Reasoning: GIVE incorporates counterfactual linkages, even when explicit relations are not present, to avoid hallucinations—a frequent issue in LLM-based reasoning tasks.
4. Progressive Answer Generation: The framework generates the final answer in a progressive manner, first relying on affirmations provided by the entity group's relations, and subsequently refining these with counterfactual insights and any external expert knowledge cached in the KG.

Experimental Findings

The paper benchmarks GIVE against other methodologies using datasets from biomedical and commonsense question answering domains. Notably, GIVE consistently outperforms others, including the state-of-the-art Retrieval-Augmented Generation (RAG) and Think-on-Graph (ToG) approaches, even when utilizing sparse knowledge bases like UMLS and ConceptNet.

• Biomedical QA: Using sparse UMLS data, GIVE enables models with limited resources, like GPT3.5-turbo, to exceed the performance of more advanced models (e.g., GPT-4) with higher internal knowledge bases.
• Commonsense QA: GIVE maintains robust reasoning capabilities regardless of the density of the KG, demonstrating superior performance using various subsets of ConceptNet.

The results also highlight GIVE's ability to boost reasoning accuracy significantly, illustrating the advantages of its counterfactual and inspiration-driven paradigm.

Implications and Future Directions

The introduction of GIVE has notable implications for structured reasoning in AI. By systematically enhancing LLM's abilities to integrate and synthesize sparse knowledge, the framework opens up new possibilities for addressing complex, domain-specific questions that previously eluded effective solutions due to insufficient training data or resource constraints.

The paper subtly hints at broader applications of this framework, where future work could extend GIVE's capabilities to other domains requiring nuanced reasoning, potentially integrating richer contextual cues and refining the calibration of "silver edges." Moreover, the authors suggest that further research might explore automated methods for optimizing intermediate conceptual pathfinding and relation extrapolation, potentially decreasing computational overhead while preserving accuracy.

GIVE stands as a testament to the potential interdisciplinary synergy between data sparsity management and cognitive reasoning models, promising strides in making heuristic computational processes more akin to human expert problem-solving.