Resilience of Large Language Models for Noisy Instructions (2024)

Abstract: As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating text across various tasks. Nonetheless, the resilience of LLMs to handle text containing inherent errors, stemming from human interactions and collaborative systems, has not been thoroughly explored. Our study investigates the resilience of LLMs against five common types of disruptions including 1) ASR (Automatic Speech Recognition) errors, 2) OCR (Optical Character Recognition) errors, 3) grammatical mistakes, 4) typographical errors, and 5) distractive content. We aim to investigate how these models react by deliberately embedding these errors into instructions. Our findings reveal that while some LLMs show a degree of resistance to certain types of noise, their overall performance significantly suffers. This emphasizes the importance of further investigation into enhancing model resilience. In response to the observed decline in performance, our study also evaluates a “re-pass” strategy, designed to purify the instructions of noise before the LLMs process them. Our analysis indicates that correcting noisy instructions, particularly for open-source LLMs, presents significant challenges.

## introduction large language models offer unprecedented capabilities in understanding and generating human-like text @xcite . built upon the foundation of pre-trained language models (plms) @xcite , large language models inherit and significantly extend the capabilities of their predecessors by following humanreadable instructions, enabling a broad spectrum of applications that were previously challenging or infeasible with unavailable training samples @xcite . meantime, the capability of llms to process noisy instructions is a critical feature that enables their applications in real-world scenarios, where data contains imperfections. to validate the extent of such occurrences, we analyzed the noise within user instructions to a chatbot. specifically, these instructions were evaluated using gpt-4 @xcite to detect the presence of the specific noise types. our statistical analysis, illustrated in figure 1 , indicates that over 40% of user inputs contain typographical errors, grammatical mistakes, or unrelated content in addition to their primary query 1 . previous research also reveals that human users are inclined to commit errors when interacting with chatbot @xcite . it is also treated as an evident social cue for human communications @xcite . therefore, examining llm's proficiency in managing noisy text inputs is critical to practical applications. in our study on deploying large language models (llms) across various applications, we categorized the kinds of noisy instructions from three primary sources. first, from a linguistic standpoint, our focus is on grammatical mistakes and typo-graphical errors. second, we explore noise stemming from system integration, specifically errors originating from optical character recognition (ocr) and automatic speech recognition (asr) technologies. lastly, we investigate the impact of destructive content from previous interactions or extended contexts. this part of our study aims to assess the models' proficiency in isolating current queries from past interactions, evaluating their effectiveness in disregarding irrelevant content. we observe distinct performance impacts across three open and closed-sourced models, when faced with different noise types. first, we find that a higher resilience of models to grammatical mistakes, likely because these errors are also present in data used for pre-training and supervised finetuning as also revealed in figure 1 . this familiarity enables models to more accurately interpret the intended meaning despite such inaccuracies. in contrast, errors from asr and ocr systems, which are less common in training datasets, present more significant challenges for the models. furthermore, our study highlights that models are susceptible to being influenced by previous instructions in both cooperative and non-cooperative manner, which can lead to deviations in responses in subsequent interactions. this suggests a limitation in the models' ability to filter out irrelevant or distracting content from past exchanges. as noisy instructions can be harmful to model perfomrnace, we investigate the potential of leveraging llms to mitigate the impact of noisy instructions through a "re-pass" strategy. this approach involves a two-step process: initially, we employ an llm to conduct zero-shot text normalization to purify the noisy instructions. next, we prompt the model to process upon the cleaned instruction. our findings reveal that not all models are adept at fulfilling this role of data normalization. the exception is chatgpt, which demonstrates a comprehensive understanding of the text and can recover the instruction with different types of noises. ## related work the progression of general-purpose large language models (llms) such as chatgpt @xcite , gemini @xcite , llama @xcite , mistral @xcite , and gemma @xcite has facilitated a myriad of real-world applications. these advancements are attributed to their capabilities in managing long-range textual dependencies, enhancing contextual comprehension, and displaying a remarkable ability to adapt to a wide array of tasks with minimal need for detailed, task-specific training. meantime, several recent studies have demonstrated that the user prompt significantly influences task performance, highlighting its indispensable role in the process @xcite . the following will introduce studies on prompt sensitivity and noisy text reconstruction as related work. ## noisy instruction and analysis in this section, we describe our methodology for integrating five types of noise into the mmlu benchmark @xcite , where the original text is devoid of any noise. we employ hybrid rulebased techniques to introduce noise for ocr and typographical errors. for asr and grammatical errors, we leverage a language model to capture error patterns and simulate these errors through a generative process. to simulate distractive content, we embed actual dialogues as irrelevant background information. an illustration of each type of noisy noise type sources asr librispeech (panayotov et al., 2015) commonvoice-15 (ardila et al., 2020) ocr nlpaug (ma, 2019) ocr engine grammatical jeleg (napoles et al., 2017) c4-200m (stahlberg and kumar, 2021) typographical nlpaug (ma, 2019) keyboard, spelling, random distractive content sharegpt (chiang et al., 2023) ## recovery of noisy instructions previous research has demonstrated the capability of language models to amend specific errors @xcite . in this section, we explore the effectiveness of utilizing large language models (llms) for zero-shot correction of four previously identified types of noise. ## conclusion in this study, we delve into the resilience of llms against noise in instructions from human interactions and system integration. this highlights the complex challenge of processing and recovering accurate information from noisy inputs. further, we investigate into the "re-pass" strategy and spot the limitations of current open-sourced models in handling noise corrections. our findings reemphasise that stronger noisy correction and resilience capabilities are required for llms, especially for system integration like asr and ocr, and process various user requests under both cooperative and no-cooperative settings.