Less is More for Improving Automatic Evaluation of Factual Consistency (2024)

Abstract: Assessing the factual consistency of automatically generated texts in relation to source context is crucial for developing reliable natural language generation applications. Recent literature proposes AlignScore which uses a unified alignment model to evaluate factual consistency and substantially outperforms previous methods across many benchmark tasks. In this paper, we take a closer look of datasets used in AlignScore and uncover an unexpected finding: utilizing a smaller number of data points can actually improve performance. We process the original AlignScore training dataset to remove noise, augment with robustness-enhanced samples, and utilize a subset comprising 10% of the data to train an improved factual consistency evaluation model, we call LIM-RA (Less Is More for Robust AlignScore). LIM-RA demonstrates superior performance, consistently outperforming AlignScore and other strong baselines like ChatGPT across four benchmarks (two utilizing traditional natural language generation datasets and two focused on large language model outputs). Our experiments show that LIM-RA achieves the highest score on 24 of the 33 test datasets, while staying competitive on the rest, establishing the new state-of-the-art benchmarks.

## introduction the emergence of large language models (llms) and an increasing interest in utilizing machinegenerated texts from like summarization, paraphrasing, and question-answering (qa) has created a need to automatically evaluate the degree to which generated natural language texts accurately reflect the factual information contained in source context. early work used natural language inference (nli) @xcite and qa @xcite to handle automatic factual consistency evaluation. however, these methods exhibit limited generalizability and struggle with handling figure 1 : ablation study on using the first n samples from each sub-train dataset for training and overall model performance. we see that the optimum benchmark performance is 452k and 1.6m samples for de-berta and roberta respectively. for comparison alignscore uses 4.7m or the first 500k. performance broken down by benchmark can be found in a.1 long contexts. recently, @xcite propose alignscore, a unified model based on roberta and is trained on a wide range of datasets to calculate the alignment between context and generated text. alignscore achieves state-of-the-art results across several factual consistency benchmarks. despite its strengths, the alignscore study has several limitations. first, the training data leveraged for developing alignscore were derived in a heuristic manner from many existing nlp tasks and datasets, adding noise and poor quality in some samples. we, therefore, ask the question: are all data points from alignscore training needed? our ablation studies shown in figure 1 indicate that the answer is "no". additionally, alignscore displays fragility regarding robustness, as it fails to identify some clear perturbations involving entities like names, numbers, etc. as context claim alignscore lim-ra [...] napoleon married the archduchess marie louise, who was 18 years old [...] archduchess marie louise was 18 years old when she married napoleon . 0.9907 0.9542 archduchess mari louze was 18 years old when she married napoleon . 0.9650 (false positive) 0.4381 the blue ridge mountains [...] attain elevations of about 2,000 ft the typical elevations of the blue ridge mountains are 2,000 ft. 0.9812 0.9434 the typical elevations of the blue ridge mountains are 2000 ft. 0.0214 (false negative) 0.8621 our experiments show that lim-ra consistently outperforms strong baselines including alignscore and gpt-3.5-turbo, achieving the new state-of-the-art on four factual consistency benchmarks covering a wide range of 33 datasets. it is worth noting that our experiments include a newly defined benchmark, large language model response (llmr), designed for evaluating llm outputs' factual consistency. lim-ra performs the best on llmr. ## experiments we conduct a comprehensive experimental study to evaluate lim-ra on multiple factual consistency benchmarks and demonstrate lim-ra consistently outperforms strong baselines and establishes new state-of-the-art results. our experiments also include ablation studies ( ## conclusions we propose lim-ra, a deberta based model to automatically evaluate factual consistency trained from a cleaner and smaller training set than used for alignscore. experimental results show lim-ra consistently outperforms the current state-ofthe-art alignscore and other strong baselines on 4 benchmarks. in addition, the model is robust to name and number variations and is better suited for llm outputs' factual consistency evaluation.