Towards Reasoning in Large Language Models: A Survey (2023)

Abstract: Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking. In recent years, large language models (LLMs) have made significant progress in natural language processing, and there is observation that these models may exhibit reasoning abilities when they are sufficiently large. However, it is not yet clear to what extent LLMs are capable of reasoning. This paper provides a comprehensive overview of the current state of knowledge on reasoning in LLMs, including techniques for improving and eliciting reasoning in these models, methods and benchmarks for evaluating reasoning abilities, findings and implications of previous research in this field, and suggestions on future directions. Our aim is to provide a detailed and up-to-date review of this topic and stimulate meaningful discussion and future work.

## introduction reasoning is a cognitive process that involves using evidence, arguments, and logic to arrive at conclusions or make judgments. it plays a central role in many intellectual activities, such as problem solving, decision making, and critical thinking. the study of reasoning is important in fields like psychology @xcite , philosophy @xcite , and computer science @xcite , as it helps individuals make decisions, solve problems, and think critically. recently, large language models (llms) @xcite @xcite , inter alia) such as chat-gpt have made significant advancements in natural language processing and related fields. it has been shown that these models exhibit emergent behaviors, including the ability to "reason", when they are large enough @xcite . for example, by providing the models with "chain of thoughts", i.e., reasoning exemplars, or a simple prompt "let's think step by step", these models are able to answer questions with explicit reasoning steps @xcite , e.g., "all whales are mammals, all mammals have kidneys; therefore, all whales have kidneys." this has sparked considerable interest in the community since reasoning ability is a hallmark of human intelligence that is frequently considered missed in current artificial intelligence systems @xcite @xcite . however, despite the strong performance of llms on certain reasoning tasks, it remains unclear whether llms are actually reasoning and to what extent they are capable of reasoning. for example, @xcite claim that "llms are decent zero-shot reasoners (p. 1)", while @xcite conclude that "llms are still far from achieving acceptable performance on common planning/reasoning tasks which pose no issues for humans to do (p. 2)." this limitation is also stated by @xcite : "we qualify that although chain of thought emulates the thought processes of human reasoners, this does not answer whether the neural network is actually reasoning (p. 9)." therefore, in this paper, we aim to provide a comprehensive overview and engage in an insightful discussion on the current state of knowledge on this fast-evolving topic. we initiate our exploration with a clarification of the concept of reasoning ( §2). subsequently, we turn our attention to the techniques for enhancing/eliciting reasoning in llms ( §3), the methods and benchmarks for evaluating reasoning in llms ( §4), and the key findings and implications in this field ( §5). finally, we reflect on and discuss the current state of the field ( §6). 2 what is reasoning? reasoning is the process of thinking about something in a logical and systematic way, using evidence and past experiences to reach a conclusion or make a decision @xcite @xcite @xcite . reasoning involves making inferences, evaluating arguments, and drawing logical conclusions based on available information. although "reasoning" is a term that is commonly used in literature and daily life, it is also an abstract concept that can refer to many things. to help the reader better understand this concept, we summarize several main categories of reasoning that are commonly recognized: deductive reasoning. deductive reasoning is a type of reasoning in which a conclusion is drawn based on the truth of the premises. in deductive reasoning, the conclusion must necessarily follow from the premises, meaning that if the premises are true, the conclusion must also be true. for example: inductive reasoning. inductive reasoning is a type of reasoning in which a conclusion is drawn based on observations or evidence. the conclusion is likely to be true based on the available evidence, but it is not necessarily certain. for example: ## towards reasoning in large language models reasoning, particularly multi-step reasoning, is often seen as a weakness in language models and other nlp models @xcite @xcite . recent research has suggested that reasoning ability may emerge in language models at a certain scale, such as models with over 100 billion parameters @xcite . in this paper, we follow @xcite in considering reasoning as an ability that is rarely present in smallscale models like gpt-2 @xcite and bert @xcite , and therefore focus on techniques applicable to improving or eliciting "reasoning" foot_1 in llms such as @xcite and palm @xcite . ## measuring reasoning in large language models we summarize methods and benchmarks for evaluating reasoning abilities of llms in this section. ## findings and implications in this section, we summarize the important findings and implications of studies on reasoning in large language models. reasoning seems an emergent ability of llms. @xcite ; @xcite show that reasoning ability appears to emerge only in large language models like gpt-3 175b, as evidenced by significant improvements in performance on reasoning tasks at a certain scale (e.g., 100 billion parameters). this suggests that it may be more effective to utilize large models for general reasoning problems rather than training small models for specific tasks. however, the reason for this emergent ability is not yet fully understood. we refer the reader to @xcite ; @xcite for some potential explanations. ## reflection, discussion, and future directions why reasoning? reasoning is the process of thinking about something in a logical and systematic way, and it is a key aspect of human intelligence. by incorporating reasoning capabilities into language models, we can enable them to perform tasks that require more complex and nuanced thinking, such as problem solving, decision making, and planning @xcite . this can improve the performance of these models on downstream tasks and increase their out-ofdistribution robustness @xcite @xcite . in addition, reasoning can make language models more explainable and interpretable, as it provides explicit rationales for their predictions. right task/application? as valmeekam et al. in addition, tasks such as solving simple math problems and concatenating letters in strings ( §4.1) are artificial and do not accurately reflect real-world situations. to truly understand the reasoning ability of llms, it is important to consider more realistic and meaningful applications such as decision making @xcite , legal reasoning (levi, 2013), and scientific reasoning @xcite . our ultimate goal should not be to enable llms to solve simple math problems, which can be simply done with other programs. when conducting relevant research, it is essential to ask whether the specific task being tackled is meaningful and whether the proposed method can be generalized to more realistic tasks and applications. are language models really able to reason? there are several indications that llms are able to reason, including 1) high performance on various tasks requiring reasoning @xcite ; for 2), although the models seem to reason stepby-step, the generated rationales may be incorrect and inconsistent. it is possible that the models are "generating reasoning-like response" rather than "reasoning step-by-step". for 3), while llms display some human-like reasoning patterns, this does not necessarily mean that they behave like humans. additionally, there are several observations that suggest llms may not be capable of reasoning: 1) llms still struggle with tasks that require complex reasoning @xcite @xcite . if llms are really decent reasoners, they should handle tasks that can be simply solved by humans through reasoning; 2) llms make mistakes in their reasoning, as explained above; 3) # foot_3 the performance of llms on downstream tasks has been found to be sensitive to the frequency of certain terms, such as numbers, in the training data @xcite , which would not be expected if the models were solving mathematical problems through reasoning; 4) # language models have been found to struggle with associating relevant information that they have memorized @xcite . overall, it is still too early to draw a conclusion about the proposed question. in fact, there is also an ongoing debate about whether language models can actually understand language or capture meaning @xcite @xcite . further in-depth analysis of factors such as training data, model architecture, and optimization objectives is needed, as well as the development of better benchmarks for measuring the reasoning capabilities of llms. however, it is clear that the current models are not yet capable of robust reasoning. ## conclusion in this paper, we have provided a detailed and upto-date review of the current state of knowledge on reasoning in large language models. we have discussed techniques for improving and eliciting reasoning in llms, methods and benchmarks for evaluating reasoning abilities, and the findings and implications of previous studies in this topic. while llms have made significant progress in natural language processing and related fields, it remains unclear to what extent they are capable of true reasoning or whether they are simply using memorized patterns and heuristics to solve problems. further research is needed to fully understand the reasoning abilities of llms, improve llms' reasoning capabilities, and determine their potential for use in a variety of applications. we hope that this paper will serve as a useful overview of the current state of the field and stimulate further discussion and research on this interesting and important topic.