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ERBench: An Entity-Relationship Based Automatically Verifiable Hallucination Benchmark for Large Language Models

Core Concepts
Utilizing relational databases and the ER model, ERBench provides a comprehensive evaluation of LLMs by generating complex and automatically verifiable questions.
ERBench proposes a benchmark for evaluating Large Language Models (LLMs) using relational databases and the Entity-Relationship (ER) model. It constructs questions based on database schema, records, and integrity constraints to assess LLM reasoning. The benchmark supports multi-hop questions, continuous evaluation, multimodal questions, and prompt engineering techniques. Experiments show GPT-4 performs best but all LLMs have room for improvement.
Large language models have achieved unprecedented performance in various applications. Existing hallucination benchmarks lack adjustable complexity for thorough analysis. ERBench converts relational databases into benchmarks based on the ER model. Foreign key constraints are used to construct multi-hop questions for LLM evaluation. GPT-4 can handle a larger variety of question types but is not perfect. Correct answers do not always imply correct rationales. ERBench supports continuous evaluation, multimodal questions, and prompt engineering techniques. Better LLM performances are observed with fine-tuning on specific datasets.
"Utilizing existing relational databases is a promising approach for constructing benchmarks due to their accurate knowledge description via functional dependencies." "We propose ERBench to automatically convert any relational database into a benchmark based on the entity-relationship (ER) model." "In our experiments, we construct an LLM benchmark using databases of multiple domains and make an extensive comparison of contemporary LLMs."

Key Insights Distilled From

by Jio Oh,Soyeo... at 03-11-2024

Deeper Inquiries

How can ERBench be extended to evaluate cross-lingual capabilities of LLMs?

ERBench can be extended to evaluate the cross-lingual capabilities of LLMs by incorporating multilingual databases into its benchmarking process. By using relational databases that contain data in multiple languages, ERBench can generate questions that require understanding and reasoning across different languages. This would involve constructing questions that involve entities or relationships spanning different language contexts, testing the LLM's ability to comprehend and respond accurately in various languages. Additionally, ERBench could introduce prompts in different languages alongside the existing questions to assess how well LLMs perform when processing information presented in a language other than their training language. By including diverse linguistic contexts within the benchmark, ERBench can provide insights into an LLM's cross-lingual capabilities and highlight areas for improvement in handling multilingual tasks effectively.

What potential biases or limitations may arise from using underlying databases in ERBench?

One potential bias that may arise from using underlying databases in ERBench is dataset bias. The content and structure of the relational databases used to construct benchmarks could reflect certain biases present in the original data sources. For example, if a database predominantly includes information from specific domains or perspectives, it may lead to biased evaluations of LLM performance on particular types of questions while neglecting others. Another limitation is related to representational bias. The design choices made when creating the schema and functional dependencies for generating questions could inadvertently introduce biases based on how entities are connected or what attributes are emphasized. This might impact the types of queries posed to LLMs and influence their evaluation outcomes. Furthermore, there could be selection bias if certain datasets are chosen over others for constructing benchmarks within ERBench. Datasets with inherent biases or limited coverage may not provide a comprehensive evaluation of an LLM's abilities across diverse knowledge domains. To mitigate these biases and limitations, careful consideration should be given to dataset selection, schema design, question generation strategies, and diversity inclusion efforts within ERBench to ensure fair and unbiased evaluations of LLM performance.

How can prompt engineering methods like Chain-of-Thought be further optimized for improved LLM performance?

Prompt engineering methods like Chain-of-Thought can be further optimized for improved LMM performance by fine-tuning the approach based on feedback from model responses during evaluation cycles. Here are some optimization strategies: Adaptive Prompt Length: Adjusting the length of prompts dynamically based on model behavior during training sessions can help optimize Chain-of-Thought effectiveness. Context Expansion: Introducing additional context cues at each step within Chain-of-Thought prompts can enhance coherence between steps and guide models towards more accurate reasoning paths. Error Analysis Feedback Loop: Analyzing errors made by models during Chain-of-Thought interactions provides valuable insights into where improvements are needed most; this feedback loop helps refine prompt structures accordingly. 4 .Diverse Demonstration Selection: Curating a diverse set of demonstrations covering various scenarios ensures robustness against overfitting while exposing models to a wide range of input patterns. 5 .Dynamic Prompt Generation: Generating prompts dynamically based on real-time model predictions allows adaptive prompting tailored specifically towards addressing current weaknesses identified during evaluation rounds. By iteratively refining these optimization strategies through continuous experimentation with varying prompt designs and monitoring model responses closely throughout training iterations will lead toward enhanced overall performance gains for optimizing Chain-of-Thought methodology applied within ERbench assessments."