link
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reflection
abstract
examine the performance of GPT-3.5 and GPT-4 models, by performing a thorough technical evaluation on different reasoning tasks across eleven distinct datasets such as deductive, inductive, abductive, analogical, causal, and multi-hop reasoning, through question-answering tasks.
Status
Done
Type
evaluation
Author
Table of contents
Some experimental resultsInductive ReasoningDeductive reasoningAbdutive ReasoningMathematical ReasoningCausal reasoningmulti-hop reasoningKey takeawaysQuotesSummary
Some experimental results
Inductive Reasoning
- it has been observed that although GPT-3.5 is a highly advanced language model, it tends to struggle more with inductive reasoning than with deductive or abductive reasoning.
- When inductive, ChaGPT-3.5 provided the correct answer by relying more on general knowledge of the real world rather than the specific given facts.
Deductive reasoning
- where the model fails to deduce the accurate response solely from the provided information. Instead, it relies on its general knowledge and understanding of the world. (和LLms not semantic reasoners文章的结论一致，trend to answer question with pre-trained knowledge rather than in-context knowledge.)
Abdutive Reasoning
Mathematical Reasoning
- observe how the model can analyze the problem in a logical sequence, but provides an incorrect answer/conclusion.
Causal reasoning
multi-hop reasoning
- conclude that ChatGPT-4 still faces challenges when integrating information from multiple sources to determine an answer.
Key takeaways
Quotes
Summary
- Our findings indicate that although there has been an improvement in the performance of GPT-4 compared to ChatGPT-3.5, there is still considerable work to be done. Specifically, areas such as inductive reasoning, mathematical problem-solving, multi-hop reasoning, and commonsense reasoning require significant attention