Industrial applications pose heightened requirements for consistency and reliability of large language models (LLMs). While LLMs are being tested with increasingly complex reasoning tasks, we argue that much can be learned via diagnostic tools that probe a fundamentally basic type of reasoning: conceptual consistency, e.g., a rule applying to “all surgeons” must also apply to “cardiac surgeons” since a cardiac surgeon is a type of surgeon. In this emerging industry track submission, we propose a method that takes concept hierarchies from a knowledge graph (KG) and automatically generates benchmarks that test conceptual consistency in LLMs. We develop a multi-domain benchmark that reveals rates of conceptual inconsistencies in several state of the art LLMs. Additionally, we use measured levels of inconsistency and disagreement in LLMs to find potentially problematic subgraphs in the reference KG. As such, it offers a scalable complement to symbolic curation, maintenance, and refinement of knowledge graphs, which is a critical activity in KG-based industrial applications.

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to evaluate each other which can be unreliable. In this paper, we provide a novel perspective where, given a dataset of prompts (viz. questions, instructions, etc.) and a set of LLMs, we rank them without access to any ground truth or reference responses. Inspired by real life where both an expert and a knowledgeable person can identify a novice our main idea is to consider triplets of models, where each one of them evaluates the other two, correctly identifying the worst model in the triplet with high probability. We also analyze our idea and provide sufficient conditions for it to succeed. Applying this idea repeatedly we propose two methods to rank LLMs. In experiments on different generative tasks (summarization, multiple-choice, and dialog), our methods reliably recover true rankings without reference data. This points to a viable low-resource mechanism for practical use.

The popularity of pretrained language models in natural language processing systems calls for a careful evaluation of such models in down-stream tasks, which have a higher potential for societal impact. The evaluation of such systems usually focuses on accuracy measures. Our findings in this paper call for attention to be paid to fairness measures as well. Through the analysis of more than a dozen pretrained language models of varying sizes on two toxic text classification tasks (English), we demonstrate that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics. Specifically, we observe that fairness can vary even more than accuracy with increasing training data size and different random initializations. At the same time, we find that little of the fairness variation is explained by model size, despite claims in the literature. To improve model fairness without retraining, we show that two post-processing methods developed for structured, tabular data can be successfully applied to a range of pretrained language models. Warning: This paper contains samples of offensive text.