Bridging Distribution Gap via Semantic Rewriting with LLM s to Enhance OOD Robustness (2024)

Abstract: This paper investigates the robustness of Large Language Models (LLMs) against Out-Of-Distribution (OOD) data within the context of sentiment analysis. Traditional fine-tuning approaches often fail to generalize effectively across different data distributions, limiting the practical deployment of LLMs in dynamic real-world scenarios. To address this challenge, we introduce a novel method called “Semantic Rewriting,” which leverages the inherent flexibility of LLMs to align both in-distribution (ID) and OOD data with the LLMs distributions. By semantically transforming sentences to minimize linguistic discrepancies, our approach helps to standardize features across datasets, thus enhancing model robustness. We conduct extensive experiments with several benchmark datasets and LLMs to validate the efficacy of our method. The results demonstrate that Semantic Rewriting significantly improves the performance of models on OOD tasks, outperforming traditional methods in both robustness and generalization capabilities. Our findings suggest that Semantic Rewriting is a promising technique for developing more reliable and versatile NLP systems capable of performing robustly across diverse operational environments.

## introduction in the dynamic field of natural language processing (nlp), large language models (llms) have shown exceptional capabilities across a spectrum of applications. nevertheless, these models frequently encounter challenges with out-of-distribution (ood) data, which can significantly hinder their effectiveness in varied real-world environments @xcite ; @xcite . conventional methods such as fine-tuning on indistribution (id) data often fail to provide robustness against the distribution shifts commonly seen in practical deployments @xcite . this paper tackles the critical challenge of bridging the distribution gap between id and ood data, essential for the robust deployment of llms. despite considerable advancements in model architectures and training techniques, the issue of distribution shift remains a significant barrier in deploying llms across diverse settings @xcite . research questions: this research stems from the research question: whether there exists any projection of id and ood data where the distribution gap is minimized, or alternatively, if there exists a global distribution from which we can sample both id and ood data. contributions: we introduce a novel method called semantic rewriting, which utilizes the flexibility of llms to align their outputs more closely with its own distribution while ensuring semantic equivalence to the original sentences. this approach involves semantically transforming sentences to standardize linguistic properties across id and ood datasets, thereby minimizing distributional discrepancies. we hypothesize that: we employ a strategy where both id and ood datasets are rewritten through a llm to standardize their stylistic and semantic features. this process not only promotes homogeneity across datasets but also enables the fine-tuned models on this transformed data to achieve markedly improved performance on ood tasks. we also use the original id and fine-tune another instance of roberta @xcite which acts as one of our baselines. our extensive experiments across benchmark dataset and llms validate our approach. the results affirm that semantic rewriting significantly bolsters model robustness against distribution shifts and, in some instances, enhances performance on id tasks. our work contributes to the broader objective of developing nlp systems that are both robust and versatile, capable of reliably operating across various domains. ## related work this section reviews relevant literature on the robustness of large language models (llms) to outof-distribution (ood) scenarios in natural language processing (nlp). our research is informed by various studies aiming to improve ood generalization through innovative methods that manipulate data and model interactions. ## our dataset to evaluate the generalization capabilities of both traditional and modern language models (lms) to out-of-distribution (ood) data, we focused on sentiment analysis as the primary nlp task. our study utilizes datasets as specified in the boss benchmark paper, which provides a framework for assessing model performance across different domains @xcite . we employ one in-distribution (id) dataset and three ood datasets, each chosen for their diverse sources and sentiment labeling schemes to comprehensively test the models under varied linguistic contexts. the datasets include: ## theoretical analysis in this approach, we are using a large language model (llm) to rewrite sentences in both indistribution (id) and out-of-distribution (ood) datasets, hypothesizing that this rewriting process will bridge the distribution gap between id and ood. here's a mathematical description and theoretical analysis of why fitting a gaussian mixture model (gmm) for original sentence embedding would result in k clusters where k is the number of datasets, and rewritten sentence embedding would result in identifying a single cluster. ## experimental validation in this section, we validate our theoretical framework by presenting empirical results obtained from the application of our sentence rewriting strategy using a large language model (llm). we analyze the impact of rewriting on the distribution of sentence embeddings from various datasets. ## baselines in this study, we evaluate the generalization capabilities of various language models (lms) to out-of-distribution (ood) datasets using different methods of prompting and rewriting. our baselines include traditional fine-tuning and more recent approaches like zero-shot prompting, and a novel method we introduce: semantic rewriting. we compare these methods across multiple datasets, including amazon as the in-distribution (id) dataset and dynasent, sst-5, and semeval as ood datasets. ## method in order to improve performance over baseline approaches, we propose semantic-rewriting followed by fine-tuning roberta @xcite with the id rewritten data and evaluate the model's performance on ood rewritten data. semantic-rewritting given a llm (large language model), l1, we feed inputs for the in distribution dataset and prompt it in a zero-shot manner to semantically rewrite the id sentences (amazon). this step involves mapping the rewritten sentence within the llms distribution. similarly we do the semantic rewriting of the ood datasets(semeval, dynasent, sst-5) and also amazon test using the same llm to map the responses to the llms distribution there by bridging the distributing gap of the id and ood datasets as now they come from the same distribution as that of the llm. ## results this section presents the findings from our experimental validation, comparing the performance of various models and methods on both in-distribution (id) and out-of-distribution (ood) datasets. the methods evaluated include traditional fine-tuning, zero-shot learning with different llama models, and our novel semantic rewriting approach. ## conclusion our extensive experiments with several benchmark datasets and llms demonstrated that semantic rewriting significantly improves the performance of models on ood tasks, outperforming traditional methods in both robustness and generalization capabilities. the results indicated a substantial reduction in centroid distances, more cohesive umap visualizations, and simplified gmm clustering, highlighting the potential of this approach to enhance model generalization.