Summary of Reading with Intent, by Benjamin Reichman et al.
Reading with Intent
by Benjamin Reichman, Kartik Talamadupula, Toshish Jawale, Larry Heck
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 This paper addresses the limitations of Retrieval Augmented Generation (RAG) systems that rely on the open internet as their knowledge source. The authors note that human communication extends beyond text-based information, encompassing intent, tonality, and connotation. Recent real-world deployments of RAG systems have struggled to understand these nuances, particularly in processing sarcasm. The paper proposes a solution by synthetically generating sarcastic passages from Natural Question’s Wikipedia retrieval corpus and testing their impact on the RAG pipeline’s performance. A prompting system is introduced to enhance the model’s ability to interpret and generate responses in the presence of sarcasm, leading to improved overall system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can better understand human language. Right now, computer systems that help us search for information on the internet have trouble understanding things like sarcasm or tone. To fix this, researchers created fake passages that sound sarcastic and tested how well a special kind of computer program could handle these kinds of texts. They also came up with a way to improve the program’s ability to understand sarcasm. This can help make our searches more accurate and useful. |
Keywords
» Artificial intelligence » Prompting » Rag » Retrieval augmented generation
