Summary of Enhancing Robustness Of Retrieval-augmented Language Models with In-context Learning, by Seong-il Park et al.
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
by Seong-Il Park, Seung-Woo Choi, Na-Hyun Kim, Jay-Yoon Lee
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary: Retrieval-Augmented Language Models (RALMs) have improved question-answering performance by incorporating external knowledge, but still struggle with unanswerable and conflicting information due to imperfect retrieval. This study introduces an in-context learning approach to enhance RALMs’ reasoning capabilities, incorporating Machine Reading Comprehension demonstrations (cases) to identify unanswerabilities and conflicts. The proposed method increases accuracy in identifying these scenarios without requiring additional fine-tuning, demonstrating the effectiveness of in-context learning for enhancing RALMs’ robustness in open-domain question-answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This study helps improve computer systems that answer questions by combining information from different sources. Current systems are good at answering most questions but struggle with those where there is no correct answer or when the answers conflict. The researchers developed a new way to teach these systems to better handle these situations. They tested their approach on two large question-answering datasets and found that it improved the system’s ability to identify cases where there was no correct answer or conflicting information. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
