Summary of Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models, by Xingyun Hong et al.
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models
by Xingyun Hong, Yan Shao, Zhilin Wang, Manni Duan, Jin Xiongnan
First submitted to arxiv on: 9 Sep 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 The development of Large Language Models (LLMs) has significantly improved their intelligence and fluency in question answering. However, the emergence of retrieval enhancement has enabled models to better utilize external information, but this also introduces noise and errors that can affect the robustness of LLMs. To address this issue, researchers constructed a dataset simulating various scenarios, including critical information absence, noise, and conflicts, based on machine reading comprehension datasets. They then proposed a data augmentation-based fine-tuning method to enhance LLM’s robustness against noise. Additionally, contrastive learning was used to preserve the model’s discrimination capability of external information. Experimental results using GPT-4 as an evaluation metric showed that the proposed methods improved model robustness while strengthening its ability to differentiate between reliable and unreliable information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart computers that can answer questions. They’re getting better at this, but they also get some wrong answers because of mistakes in the information they find from the internet. To make them more accurate, scientists created a special dataset with lots of scenarios that might happen in real life, like missing important details or having incorrect information. They then came up with a way to improve LLM’s accuracy by giving it more training and helping it learn how to distinguish between correct and incorrect information. This new method was tested using a computer program called GPT-4 and showed that it can make LLMs even better at answering questions correctly. |
Keywords
» Artificial intelligence » Data augmentation » Fine tuning » Gpt » Question answering
