Summary of To Trust or Not to Trust? Enhancing Large Language Models’ Situated Faithfulness to External Contexts, by Yukun Huang et al.
To Trust or Not to Trust? Enhancing Large Language Models’ Situated Faithfulness to External Contexts
by Yukun Huang, Sanxing Chen, Hongyi Cai, Bhuwan Dhingra
First submitted to arxiv on: 18 Oct 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 Large Language Models (LLMs) are often augmented with external contexts, such as those used in retrieval-augmented generation (RAG). However, these contexts can be inaccurate or intentionally misleading, leading to conflicts with the model’s internal knowledge. To address this issue, we propose two approaches: Self-Guided Confidence Reasoning (SCR) and Rule-Based Confidence Reasoning (RCR). SCR enables models to self-assess the confidence of external information relative to their own internal knowledge to produce the most accurate answer. RCR, in contrast, extracts explicit confidence signals from the LLM and determines the final answer using predefined rules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are like super smart computers that can understand language. Sometimes they get wrong information from outside, which makes it hard for them to give good answers. Scientists want these models to be better at figuring out what’s true and what’s not. They came up with two ways to help: Self-Guided Confidence Reasoning (SCR) and Rule-Based Confidence Reasoning (RCR). SCR helps the model decide if outside information is right or wrong based on its own knowledge. RCR uses rules to make decisions. Some models work better with one method than the other. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
