Summary of On the Role Of Model Prior in Real-world Inductive Reasoning, by Zhuo Liu et al.
On the Role of Model Prior in Real-World Inductive Reasoning
by Zhuo Liu, Ding Yu, Hangfeng He
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have demonstrated impressive abilities in generating hypotheses that can generalize effectively to new instances when guided by in-context demonstrations. However, their hypothesis generation is not solely determined by these demonstrations but is significantly shaped by task-specific model priors. This study bridges this gap by systematically evaluating three inductive reasoning strategies across five real-world tasks with three LLMs. The findings reveal that hypothesis generation is primarily driven by the model’s inherent priors; removing demonstrations results in minimal loss of hypothesis quality and downstream usage. Furthermore, the results show consistency across various label formats with different label configurations, highlighting the potential for better utilizing model priors in real-world inductive reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart computer that can make predictions based on what it’s learned from data. These computers are called Large Language Models (LLMs). Researchers have found that when these computers generate ideas or hypotheses, it’s not just because of the specific examples they’ve seen before. Instead, their own built-in biases and assumptions play a big role too. This study looked at how LLMs come up with ideas and found that most of the time, their own biases are what drive the outcome. Even if you take away the specific examples, the computer’s predictions don’t change much. This is important to know because it could help us use these computers more effectively in real-world situations. |
