Summary of Is There No Such Thing As a Bad Question? H4r: Hallucibot For Ratiocination, Rewriting, Ranking, and Routing, by William Watson et al.
Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing
by William Watson, Nicole Cho, Nishan Srishankar
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 tackles the issue of hallucination in Large Language Models (LLMs) by introducing HalluciBot, a model that predicts query propensity for hallucination before generation. Unlike prior studies focusing on post-generation refinement, HalluciBot estimates query quality based on accuracy and consensus. The approach demonstrates 95.7% output accuracy for Multiple Choice questions using query rewriting guided by empirical estimates. The training procedure involves perturbing queries, employing multiple LLM agents, conducting a Multi-Agent Monte Carlo simulation, and training an encoder classifier. HalluciBot’s ablation studies show an increase in output diversity (+12.5 agreement spread) through lexical but semantic query perturbations. This work paves the way for ratiocinating (76.0% test F1 score), rewriting (+30.2%), ranking (+50.6%), and routing queries to effective pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that Large Language Models don’t make mistakes when answering questions. The authors created a new model called HalluciBot, which helps predict when the model might make a mistake. They tested it with multiple-choice questions and found that it can get the answers right 95.7% of the time! This means that if you ask the model to answer a question, it will give you an accurate response most of the time. The authors also found that they could improve the model’s performance by making slight changes to how the queries are asked. |
Keywords
» Artificial intelligence » Encoder » F1 score » Hallucination
