Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism (2023)

Abstract: Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style reasoning robustly is of interest from a probing perspective. In this study, we inspect the step-by-step reasoning ability of LLMs with a focus on negation, which is a core linguistic phenomenon that is difficult to process. In particular, we introduce several controlled settings (e.g., reasoning in case of fictional entities) to evaluate the logical reasoning abilities of the models. We observed that dozens of modern LLMs were not robust against lexical negation (e.g., plausible \rightarrow implausible) when performing CoT-style reasoning, and the results highlight unique limitations in each LLM family.

## introduction few-shot learning @xcite has led to a remarkable performance in large language models (llms). in particular, instructions to generate a reasoning process along with the answer, i.e., chainof-thought (cot) prompting @xcite , have improved the performance of llms. building on this, the ability of llms to perform cot-style reasoning robustly is of interest from the probing perspective-how correctly these models perform step-by-step reasoning?; however, to the best of our knowledge, deeper analyses have yet to be explored fully. to address this question, this study investigates the step-by-step reasoning ability of llms with a special focus on robustness against (lexical) negation. historically, negation has been challenging for neural models @xcite , and determining whether the step-by-step reasoning of llms overcomes this limitation is important in the natural language processing (nlp) community. is the following sentence implausible? is the following sentence plausible? may is a turboglide player. stepover happens in turboglide. the answer is the model must answer no to the latter question about the implausibility of the sentence based on the valid logical flow. here, to evaluate the robust logical skill separately, the controlled reasoning chain is given, and the model must derive the answer based solely on the validity of the logical flow without commonsense knowledge due to fictional entities, e.g., turboglide. our controlled experiments using dozens of llms, including @xcite , demonstrate that such models deteriorate in performance substantially when processing questions involving words with just a negative prefix, e.g., implausible, unreasonable (figure 1 ). in addition, the results show that each llm family has its unique biases against lexical negation, which suggests that different llm training settings produce substantial differences under certain conditions, and the problems to be addressed are model-dependent. these issues clarify the exact weakness of modern llms. ## reasoning against lexical negation given a chain of the reasoning process, we expect the llm to derive a logically valid conclusion even when the problem involves lexical negation (figure 1 ). in section 2.1, we introduce the task format, and section 2.2 elaborates on the controlled task settings to elucidate the abilities of the models. note that our task is similar to cot reasoning; however, we provide the models with predefined reasoning chains to facilitate controlled analyses. ## results, analyses, and discussions tables 2 and 3 show the average and standard deviation of accuracy and no-ratio of each model in the sp, oc, and wt tasks. ## conclusions in this study, we have investigated the ability of llms to derive valid conclusions given a reasoning chain with a (lexical) negation, a historically tough phenomenon for neural models. the results of multi-difficulty controlled experiments revealed that llms with cot-style prompting struggled to address negation; a simple flip of one word (e.g., @xmath0. in addition, we have found consistent, systematic failure patterns unique in each llm family. for example, some models always answered no to different question settings. in the future, we plan to analyze the model's internal and explore the source of this weakness.