Leveraging Structured Knowledge to Automatically Detect Hallucination in Large Language Models

Large Language Models has sparked a revolution in AI’s natural language capabilities. These foundation models can generate impressively coherent text on practically any topic when prompted.

However, concerns around factual consistency and hallucinated content have accompanied their rise.

Despite strong performance on closed domain datasets, open-ended queries can expose distortions in an LLM’s world knowledge.

For instance, LLMs may generate plausible but incorrect answers by confusing entities, relations or temporal events.

Or they may conflate details from disjoint contexts when operating beyond their training distribution. These factual inaccuracies point to fundamental limitations around reasoning on open domains.

While measurement benchmarks help quantify an LLM’s factual gaps post-training, the focus now needs to shift towards runtime monitoring and verification in real-world deployments.

As organizations increasingly integrate conversational interfaces powered by LLMs, maintaining alignment with truth is critical for reliability and trust. Manual fact-checking is expensive, lacks throughput and proves infeasible for niche domains.

This article proposes an approach for automated hallucination detection by comparing LLM inferences against structured knowledge graphs (KGs). KGs act as an external memory backbone, encoding relational facts about entities and events.

By continuously extracting assertions from LLM responses and matching them against such a domain-specific KG, contradictions can point to potential hallucinations. Tracking this metric over time provides a holistic view into factual drift.

KGs provide fixed positional references for assessing deviations within an LLM’s fluid generative space. Combining the strengths of neural representation learning and symbolic knowledge anchoring paves the path ahead for not just detecting but also correcting departures from reality. Achieving safe and trustworthy language AI necessitates sustained research on these fronts as LLMs continue advancing.

Building a high-performing retrieval-augmented generation (RAG) system that continuously improves requires implementing an effective data flywheel. This Virtuous cycle of instrumentation, analysis, tracing issues to data gaps, improving underlying data sources, and iteration can significantly enhance systems leveraging knowledge graphs and large language models for question answering at inference time. By systematically detecting problematic responses and expanding the knowledge graph to address deficiencies, the data flywheel enables such systems to incrementally learn in a managed, targeted way.

Specifically, this methodical flywheel pipeline is highly relevant for knowledge graph augmented large language models deployed in conversational search, customer support, and other domains necessitating reliable question-answering grounded in factual knowledge. By tracing poor responses during usage back to missing entities, relations or facts in the integrated knowledge substrate, targeted augmentation and fine-tuning can improve performance and trustworthiness. The flywheel effect also reduces manual oversight needs by codifying the improvement loop. Orchestrating representation learning, structured knowledge and usage-driven enhancement is key to scalable progress.

This article will explore in depth how the continuous inspection and refinement process powered by the data flywheel can make knowledge graph-based large language models more adaptive and aligned with actualities over their deployment lifetime. The techniques provide a template for automated, needs-based learning critically important for industrial-grade reliability.

The Need for Automated Hallucination Detection

As large language models continue maturing, their integration into real-world applications is accelerating across domains like customer support, content generation and conversational search. However, practical deployment surfaces fresh challenges around reliability. Without rigorous validation safeguards, inaccuracies in an LLM’s inferences risk compromising procedural integrity and trust.

For instance, incorrect entity or event details in an auto-generated social media post can dilute organizational credibility. Confusing facts in a conversational interface can misguide users and induce unreliable workflows. Such factual distortions may stem from inadequate world knowledge, noisy training data or mismatches with the deployment distribution. Regardless of origins, undetected hallucinations in model-generated text can undermine value addition goals.

While measurement studies help characterize weaknesses post-training, the priority is shifting towards continuous verification in operational settings. But manual fact-checking by domain experts has massive throughput limitations. Building automated mechanisms for dynamic monitoring of hallucinations is therefore critical. By tracing divergences from ground truth in real-time and defining appropriate guard rails, LLMs can be deployed safely. But this necessitates structured knowledge substrates tailored to specialized domains that capture diverse entities, relations and temporal events.

Constructing exhaustive verification sources for narrow domains poses cost and latency barriers. However, recent research shows combining curated knowledge graphs with neural language models helps assess factuality and identify knowledge gaps. Extending similar techniques for low-latency production pipelines can make automated hallucination detection feasible. The subsequent sections elaborate an approach centered around this idea. Sustaining users’ trust in AI applications requires bolstering LLMs with factual integrity. The path forward lies in orchestrating structured knowledge with neural representations to uphold this principle.

Augmenting Detection using Question Answering

The KGLens paper proposes an intriguing technique that automatically transforms a domain knowledge graph into a suite of natural language questions for evaluating language models. By sampling entity-relation edges and using a question generator module, both fact-checking and fact-answering evaluations are enabled over a wide set of structured queries.

We can extend this question generation strategy to diversify the inputs fed into our hallucination detection pipeline. Instead of plain user text, reformulating parts of the knowledge graph into questions poses an additional challenge for the LLM to respond accurately while retaining consistency with the encoded facts. Inconsistencies in the LLM’s responses to such diverse question formats helps expose factual gaps.

Specifically, a module can periodically resample subgraphs and generate Yes/No and WH- type natural language questions querying those entity relationships. The pipeline extracts responses and matches against the KG as before. But now contradictions signal errors in the open-ended inferences as well as question-answering capacity.

Incorporating Logical Rules

The ChatRule paper demonstrates using prompt-based techniques to elicit logical rules relating entities and events from large langauge models. Such symbolic rules serve as interpretable validators for predicting and explaining distortions in LLMs.

We can analyze the space of logic rules mined by prompting the target LLM itself using our domain knowledge graph as context. Prompting templates help generate rule proposals that undergo crowd-sourced filtering. The validated rules further expand our KG. At runtime, flagged contradictions get traced back to the encoded rules they violate. Aggregate statistics reveal which logical constraints are frequently broken. This enhances explainability and precision by blending symbolic logic with neural representations.

The Core Idea

The proposed approach for detecting factual inconsistencies from large language models involves constructing a domain-specific knowledge graph (KG) to serve as the ground truth and point of comparison at runtime.

A knowledge graph structurally represents concepts (entities) and their relationships (predicates) as triples through a connected network. Encoding curated facts and logic rules over key entities builds highly specialized KGs spanning niche domains. For instance, in healthcare, a KG may capture drugs, adverse events, timestamps, dosage guidelines and other ontology-based connections.

Augmenting the KG with logical rules enables expanding its scope through systematic inference. By mining associations from domain text and encoding them as Horn clauses, new factual statements get derived. For example, the rule “interacts-with(X,Y) ∧ warns-about(Y,Z) ⇒ warns-about(X,Z)” infers additional warning side-effects. Such rule-based enhancement enriches the KG with inferred yet verifiable knowledge.

At inference time, user prompts invoke large language model (LLM) responses. These responses reflecting dynamic generation from the LLM’s learned representations are then compared against the static KG. Factual statements get extracted from the LLM responses using syntactic parsing and entity/relation extraction. The entities are linked to the KG vocabulary using normalization techniques like exact match, synonym mapping etc.

Finally, aligning the extracted statements against the KG reveals matches and contradictions. Matches represent properly encoded knowledge, while contradictions point to potential hallucinations. By tracking contradictions over many LLM query exchanges, we can empirically pinpoint factual drift. Further, rule-wise statistics would reveal which logic constraints are being violated often, providing explainability.

This methodology blends structured knowledge with neural representation learning for continuous hallucination monitoring. The subsequent sections illustrate constructing specialized KGs, architecting the runtime pipeline, and extending this approach across languages.

Constructing the Knowledge Graph

The first step is identifying suitable knowledge sources that provide structured data about key entities and relationships for the target domain. Collaborative knowledge bases like Wikidata, DBpedia, ConceptNet as well as domain-specific ontologies are queried to extract relevant semantic triples. For example, in pharmaceutical research, existing medical ontologies describe various drugs, protein interactions, disease associations, and adverse events.

Next, the aggregated facts demand careful filtering and cleaning focused on information precision. Techniques like constrained random walks over the extracted subgraphs enable controlled expansion centered around pivotal entities while retaining representativeness of the original distribution. The random walks gather connected entities and relations while preventing traversal into irrelevant territories. Statistical metrics identify popular regions versus sparse areas to prune stray fragments. Any conflicts arising from integrations across sources get resolved as well.

Further, manual examination by subject matter experts roots out remaining errors and misinformation. Additional user input clarifies ambiguities across literatures to formulate the cleansed seed knowledge graph. Then comes manual encoding of domain logic rules around salient relations into logic programming languages like Prolog, Datalog or linear Horn clauses. These inference rules expand the KG by deducing new yet valid statements. For example, a rule may dictate that if a virus X affects tissue Y, and drug Z targets virus X, then drug Z treats the effects of tissue Y.

Fusing statistical linking with human-in-the-loop supervision results in a high quality KG focused specifically on entities and relations that need factual verification for the application domain. The expanded KG encompassing seed facts, bindings and inferences serves as the ground truth for assessing language model deviations at runtime. With relevance and precision as the central goals, meticulous construction of this specialized KG is key before operationalization.

Architecting the Pipeline

A scalable microservice pipeline handles the end-to-end flow — from receiving user inputs to detecting hallucinations by comparing LLM responses with the KG:

It utilizes modular components for each stage:

• User Input Processing: Handles concurrent requests and routes to backend LLMs.
• LLM Inference: Generates responses for inputs using high-capacity GPU clusters.
• Information Extraction: Extracts relational triples of key entities and relations.
• Entity Linking: Maps extracted entities to nodes in the KG.
• Graph Reasoning: Matches graph patterns and identifies contradictions.
• Hallucination Detection: Flags responses contradicting the KG.
• Metric Tracking: Calculates alignment metrics over time.
• Alerting: Triggers notifications when metric thresholds are exceeded.

Jointly, these components enable automated, scalable and continuous hallucination monitoring by comparing LLM outputs with the ground truth KG.

The Road Ahead

As large language models rapidly advance in capabilities, maintaining alignment with factual knowledge poses an evolving challenge. While the techniques outlined earlier provide an initial framework for monitoring hallucinations using structured knowledge graphs, significantly more research across multiple fronts is imperative to realize robust and trustworthy language AI.

A key bottleneck today involves expertise-intensive manual construction of domain knowledge graphs and encoding validation rules. An opportunity exists to automate parts of this process by generating logic rules using transformer models. For instance, few-shot prompts based on seed graphs may elicit recursive rule proposals from models. Crowdsourcing user inputs then help filter meaningful rules, providing more Supervised learning signal.

However, relying solely on manual fact collection risks completeness issues. Hence continually expanding the knowledge graph itself from external datasets and model inferences is vital. But this necessitates optimizing graph analytics for scalability. Distributed storage across commodity hardware speeds up ingestion and querying. Serverless architectures dynamically scale complex traversals.

More broadly, a comprehensive framework should consolidate multi-format data encompassing images, videos, speech and text rather than just symbolic triples or rules. Scene graphs integrated using perceptual modules afford richer context when interpreting language. Causal relations gleaned from time-series datasets impose more constraints on reasoning. Grounded learning objectives using such multisensory knowledge teach models to embed basic physical and social intutions.

Human oversight also plays a pivotal role in debugging model limitations using empirical responses. Interfaces soliciting user ratings on generations build training sets for precision tuning. Formalizing feedback as semantic parse trees then enables targeted parameter updates via distribution alignment. Such human-AI interaction cycles thus compound improvements over time while centering value alignment.

Sustained progress necessitates cross-disciplinary initiatives unifying grounding knowledge in multimodal substrates; automated enhancement through neuro-symbolic orchestration and human-guided learning. The fusion promises more robust language models tightly coupled with world knowledge.

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Chief AI Officer & Architect : Builder of Neuro-Symbolic AI Systems @Fribl enhanced GenAI for HR