Summary of Confidence-aware Sub-structure Beam Search (cabs): Mitigating Hallucination in Structured Data Generation with Large Language Models, by Chengwei Wei et al.
Confidence-Aware Sub-Structure Beam Search (CABS): Mitigating Hallucination in Structured Data Generation with Large Language Models
by Chengwei Wei, Kee Kiat Koo, Amir Tavanaei, Karim Bouyarmane
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Large Language Models (LLMs) have enabled the creation of structured data, with applications in domains such as tabular data, document databases, and product catalogs. However, concerns persist about the veracity of generated data due to incorrect references or hallucinations, necessitating confidence estimation methods for mitigation. This paper investigates confidence estimation methods for generated sub-structure-level data, introducing the concept of Confidence Network that applies on the hidden state of the LLM transformer as a more targeted estimate than traditional token conditional probability. Additionally, the paper proposes Confidence-Aware sub-structure Beam Search (CABS), a novel decoding method operating at the sub-structure level in structured data generation. CABS enhances the faithfulness of structured data generation by considering confidence scores from the Confidence Network for each sub-structure-level data and iteratively refining prompts. The results show that CABS outperforms traditional token-level beam search for structured data generation, with an average recall of 16.7% at 90% precision on the problem of product attribute generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving the way Large Language Models create structured data, like tables or lists. Right now, there are concerns that this generated data might not be accurate because it can include false information. To solve this problem, the researchers in this paper developed new methods to estimate how confident a model is in its predictions. They tested these methods and found that one of them, called Confidence-Aware sub-structure Beam Search (CABS), works really well. CABS makes sure that the generated data is accurate by considering how confident the model is in each piece of information and refining it until it’s right. |
Keywords
» Artificial intelligence » Precision » Probability » Recall » Token » Transformer
