Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
ChatGPT has shown its great power in text processing, including its reasoning ability from text reading. However, there has not been any direct comparison between human readers and ChatGPT in reasoning ability related to text reading. This study was undertaken to investigate how ChatGPTs (i.e., ChatGPT and ChatGPT Plus) and Chinese senior school students as ESL learners exhibited their reasoning ability from English narrative texts. Additionally, we compared the two ChatGPTs in the reasoning performances when commands were updated elaborately. The whole study was composed of three reasoning tests: Test 1 for commonsense inference, Test 2 for emotional inference, and Test 3 for causal inference. The results showed that in Test 1, the students outdid the two ChatGPT versions in local-culture-related inferences but performed worse than the chatbots in daily-life inferences. In Test 2, ChatGPT Plus excelled whereas ChatGPT lagged behind in accuracy. In association with both accuracy and frequency of correct responses, the students were inferior to the two chatbots. Compared with ChatGPTs' better performance in positive emotions, the students showed their superiority in inferring negative emotions. In Test 3, the students demonstrated better logical analysis, outdoing both chatbots. In updating command condition, ChatGPT Plus displayed good causal reasoning ability while ChatGPT kept unchanged. Our study reveals that human readers and ChatGPTs have their respective advantages and disadvantages in drawing inferences from text reading comprehension, unlocking a complementary relationship in text-based reasoning.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 17 of its 58 references that are in the map. First posted on arXiv 17 Nov 2023.
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17 of 58 references.
Closest ancestors · 2
Same direction, newest first
- 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-47 Apr
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity8 Feb
Other references · 15
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
- 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 472
- 2019KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoningcited by 216
- 2020A Survey on Causal Inferencecited by 202
- 2023Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Taskscited by 88
- 2018Event2Mind: Commonsense Inference on Events, Intents, and Reactionscited by 82
- 2019Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approachescited by 64
- 2018How to Make Causal Inferences Using Textscited by 59
- 2023Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questionscited by 42
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