Summary of Too Big to Fool: Resisting Deception in Language Models, by Mohammad Reza Samsami et al.
Too Big to Fool: Resisting Deception in Language Models
by Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez, Megh Thakkar, Sarath Chandar, Maxime Gasse
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models’ ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how big language models handle misleading information given with prompts. They find that bigger models are better at ignoring fake clues and following real instructions. The researchers think this is because bigger models are more skilled at using what they already know to make sense of the prompt, rather than just memorizing things. |
Keywords
» Artificial intelligence » Prompt
