The world’s leading publication for data science, AI, and ML professionals.

Stepping Stones to Understanding: Knowledge Graphs as Scaffolds for Interpretable Chain-of-Thought…

Artificial intelligence software was used to enhance the grammar, flow, and readability of this article's text.

Artificial intelligence software was used to enhance the grammar, flow, and readability of this article’s text.

Large language models (LLMs), trained on vast volumes of text data, has sparked a revolution in AI. Their ability to generate remarkably eloquent, coherent language simply from a short text prompt has opened up new horizons across domains from creative writing to conversational assistants.

However, mastery of linguistic expression alone does not equate true intelligence. LLMs still lack semantic understanding of concepts and logical reasoning abilities required for situational comprehension and complex problem solving. Their knowledge remains confined to superficial patterns discerned from training corpora rather than grounded facts about the real world.

As we pose more open-ended, multifaceted questions to these models, their limitations become increasingly apparent. They cannot logically synthesize details from different documents or make inferences spanning multiple steps to derive answers.

Once queries start departing from the distribution of training Data, hallucinated or contradictory responses start emerging.

To address these pitfalls, the AI community has pivoted focus toward retrieval augmented generative (RAG) frameworks. These systems aim to synergize the linguistic prowess of language models with fast, targeted access to external knowledge sources that can ground them in factual context instead of hallucinations.

Most existing architectures retrieve supplementary information using semantic similarity over vector representations of passages from text corpora. However, this struggles with fuzzy relevance between retrieved passages and actual query context. Key details get lost when condensing passages to singular opaque vectors devoid of contextual links. Deriving coherent narratives tying disparate facts through logical reasoning remains arduous.

This underscores the need for incorporating structured knowledge sources that encapsulate real-world entities and relationships between them. Knowledge graphs meet this need – encoding facts as interconnected nodes and edges that can be traversed along explanatory pathways. However, effectively grounding free-form reasoning of language models upon structured graphs presents interfacing challenges. Creatively bridging neural approaches with symbolic representations remains an open problem.

An emerging technique that offers promise in this direction is chain-of-thought (CoT) prompting. CoT nudges language models to reveal their reasoning in step-by-step inferential chains. Each connection becomes plainly visible, enhancing transparency. However, coherence rapidly falls apart over long histories in purely free-form linguistic spaces. Knowledge graphs could provide the missing scaffolding to give structure to these unraveling reasoning trajectories.

Explicitly tracing CoT steps along knowledge graph pathways may enable logically sound reasoning firmly grounded in chains of facts. However, finding the right alignments between unstructured neural outputs and structured symbolic knowledge remains an open challenge. Innovations in this direction offer hope for blending strengths of both approaches – symbolic representations with sound deductive chains anchored to real-world entities connected fluidly through vector spaces allowing efficient statistical inference.

The rest of the article will explore this promising intersection of knowledge graphs and CoT reasoning within LLMs for more robust situational intelligence. We delve into techniques leveraging each approach’s complementary strengths while mitigating their weaknesses in isolation.

I. Knowledge Graphs for Robust Few-Shot Learning

New Research Proves Knowledge Graphs Drastically Improve Accuracy of Large Language Models on…

Vector Search Is Not All You Need

Most existing RAG systems rely solely on passage embeddings for semantic similarity matching. However, these struggle with fuzzy relevance and inability to jointly analyze connected facts scattered across passages. Knowledge graphs address this by retaining symbolic facts and relationships enabling explainable multi-hop reasoning.

Diverse Graph Algorithms for Versatile Reasoning

Knowledge graphs equip us with an entire new repertoire of algorithms optimized for different reasoning modalities:

  • Graph traversal algorithms like Personalized PageRank allow flexible associative reasoning by analyzing indirect connections between entities. This supports deducing new relations from inference chains spanning multiple edges.
  • Algorithms tuned for search (e.g. Approximate Nearest Neighbors) allow efficiently querying facts related to specific entities. This aids precise factual retrieval.
  • Graph summarization algorithms can concisely distill subgraphs with the most pertinent information to simplify reasoning. This reduces noise and improves focus.

Optimized Knowledge Graph Embeddings

In addition, knowledge graph elements like entities, relations and text can be encoded into vector spaces as well, enabling mathematical operations:

  • Transitive embeddings improve deductive reasoning across multi-hop inference chains by maintaining equivalence across composition of relations.
  • Hierarchical embeddings encode taxonomy hierarchies between entities, allowing inheritance-based reasoning. This inherits facts from ancestral classes.
  • Contextual embeddings from large transformer language models capture semantic nuances in textual attributes of entities and relations.

The partnerships between rich symbolic representations and flexible vector spaces provide the best foundations for few-shot learning – neatly structured knowledge to scaffold explicit logical deductions explicated through dynamically fluid vectors spaces that distill salient patterns.

II. Augmenting Chain-of-Thought with Structured Knowledge Graphs

Achieving Structured Reasoning with LLMs in Chaotic Contexts with Thread of Thought Prompting and…

Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

Chain-of-thought (CoT) prompting guides language models to reveal their reasoning in explanatory chains of inferential steps. However, as inferences span longerhistories, coherence often unravels in free-form linguistic space. Knowledge graphs can provide missing structure.

Explicitly Encoding Concepts and Relations

Knowledge graphs encode concepts as interlinked symbolic nodes, capturing relations between them. Traversing explanatory pathways over this explicit network can scaffold CoT reasoning. Recent approaches like Graph-of-Thoughts (GoT) explore assembling situational graphs to model evolving CoT steps.

Beyond just modeling, the structured representations can participate in steering the reasoning:

  1. Querying Ontologies: Initial ontology queries establishing definitions and high-level context can frame the reasoning.
  2. Traversing Relations: Graph algorithms can gather connected facts relevant for each CoT step.
  3. Updating Representations: Embeddings encode extracted details, focussing attention.

Coordinating Hybrid Reasoning

Integrating neural CoT prompting with structured knowledge graphs requires coordinating distributed reasoning modules:

  1. Manager: Sequences module execution, balancing language model requests with retrieval.
  2. Prompter: Elicits free-form rationales from language models through CoT prompting.
  3. Retriever: Gathers pertinent graph details using algorithms like Personalized PageRank.
  4. Parser: Transforms retrieved facts to natural language or vectors.
  5. Scorer: Assesses relevance of retrieved facts for current context.
  6. Fuser: Combines salient knowledge with updated prompt for next round.

The modular architecture allows combining strengths of neural and symbolic approaches. The language model thinks fluidly while the graph preserves logic – each compensating for the other’s limitations.

Choreographing Hybrid Reasoning

Orchestrating the staged interplay between structured knowledge and fluid vector inferences is key to unlocking new reasoning capabilities combining:

  • Interpretability of explicit symbolic modeling with scalable pattern recognition using embeddings.
  • Logical soundness of axiomatic knowledge with adaptive improvisation of neural approaches.
  • Explainability afforded by graph traversals with efficient computation via vectors.

Innovations on this synthesis promise more reliable, versatile and transparent reasoning than possible with either approach in isolation. The partnerships open new frontiers for situated intelligence.

III. Current Gaps and Future Directions

Knowledge Graph Transformers: Architecting Dynamic Reasoning for Evolving Knowledge

Unlocking the Power of Graphs for AI Reasoning

While knowledge graphs offer promise for grounding reasoning, realizing their full potential has obstacles like sub-optimal construction, alignment, personalization and handling evolution:

Comprehensive, High-fidelity Knowledge Graphs

Manually curating extensive high-quality knowledge graphs spanning diverse nuanced domains poses scaling bottlenecks. Meanwhile open-source graphs suffer from sparsity, inconsistency and noise issues ill-suited for supporting sound deductive chains. Clean ontological knowledge combined with noisy web extractions brings integration difficulties.

Smoothly Integrating Vector and Symbolic Spaces

Bridging the symbolic structure of knowledge graphs with latent vector spaces of language models to enable seamless exchange of information is non-trivial. Simple approaches like direct vector lookups struggle to adequately capture symbolic semantics. More advanced techniques like graph neural networks show promise for elegantly grounding graphs within language model vector spaces to enable tightly coupled reasoning. But research remains nascent.

Personalized and Current Temporal Graphs

Static knowledge graphs poorly reflect individual users’ unique contexts, hindering personalized reasoning aligned with personal world knowledge. Constructing customizable user-specific knowledge graphs remains prohibitively expensive. Meanwhile, static graphs also grow obsolete, failing to track constantly shifting real-world states and events critical for contemporary reasoning. Dynamic graphs accurately mirroring our ephemeral environments are pivotal.

Exploring Innovations to Overcome Limitations

Nevertheless innovations hold promise in mitigating these limitations through fusion techniques combining curated and extracted knowledge, improved grounding algorithms deeply intertwining symbolic and neural reasoning, customizable dynamic graph construction aided by smart assistants, and stream learning continuously updating representations – all coming together to realize the symbiotic potential of integrated reasoning.

Sources :

Igniting Language Intelligence: The Hitchhiker’s Guide From Chain-of-Thought Reasoning to Language…


Related Articles