Analysis of Plan-based Retrieval for Grounded Text Generation

TL;DR

This paper explores how plan-guided retrieval in instruction-tuned language models can reduce hallucinations in grounded text generation by improving factual accuracy and source attribution.

Contribution

It introduces a novel approach using planning to guide retrieval in language models, enhancing factual correctness in long-form text generation.

Findings

Plan-guided retrieval reduces hallucination frequency.

Improves factual coverage and source attribution.

Produces more informative and accurate responses.

Abstract

In text generation, hallucinations refer to the generation of seemingly coherent text that contradicts established knowledge. One compelling hypothesis is that hallucinations occur when a language model is given a generation task outside its parametric knowledge (due to rarity, recency, domain, etc.). A common strategy to address this limitation is to infuse the language models with retrieval mechanisms, providing the model with relevant knowledge for the task. In this paper, we leverage the planning capabilities of instruction-tuned LLMs and analyze how planning can be used to guide retrieval to further reduce the frequency of hallucinations. We empirically evaluate several variations of our proposed approach on long-form text generation tasks. By improving the coverage of relevant facts, plan-guided retrieval and generation can produce more informative responses while providing a higher rate of attribution to source documents.