Summary of Scope Ambiguities in Large Language Models, by Gaurav Kamath et al.
Scope Ambiguities in Large Language Models
by Gaurav Kamath, Sebastian Schuster, Sowmya Vajjala, Siva Reddy
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates how large language models, specifically autoregressive models like GPT-2, GPT-3/3.5, Llama 2, and GPT-4, handle scope ambiguities in sentences. Scope ambiguities occur when multiple semantic operators with overlapping scope create interpretation difficulties. Despite the significance of this issue in language processing, there has been little research on how modern large language models address these challenges. The authors introduce novel datasets containing over 1,000 unique scope-ambiguous sentences, annotated for human judgments. They find that several models are sensitive to meaning ambiguities and can accurately identify human-preferred readings at a high level (over 90% in some cases). This research contributes to our understanding of the interaction between semantic structure and world knowledge in language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers understand sentences that have multiple meanings. These “scope ambiguities” can be tricky for humans to figure out, too! The researchers want to know if popular computer models like GPT-2, GPT-3/3.5, Llama 2, and GPT-4 can handle these ambiguous sentences well. They created a bunch of example sentences (over 1,000!) that have multiple meanings and asked humans which meaning makes the most sense. Then, they compared how these computer models do against human judgment. The results show that some of these computer models are pretty good at figuring out the right meaning – in fact, over 90% of the time! |
Keywords
» Artificial intelligence » Autoregressive » Gpt » Llama
