Analysis of Plan-based Retrieval for Grounded Text Generation (2408.10490v1)

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 LLM 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 LLMs 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.

An Analysis of Plan-based Retrieval for Grounded Text Generation

The paper "Analysis of Plan-based Retrieval for Grounded Text Generation" by Ameya Godbole and colleagues, investigates a significant problem within the domain of natural language generation: hallucinations in LLM outputs. Hallucinations, which are syntactically and semantically coherent statements but factually inaccurate, present a substantial limitation for the deployment of LLMs in applications demanding high factual accuracy, such as summarization, dialogue, and translation.

Research Context and Objectives

The research posits that hallucinations largely occur when models are tasked with generating information outside their parametric knowledge. This limitation is due in part to factors like the rarity, recency, or domain specificity of required information. A prevalent mitigation approach has been the augmentation of LLMs with retrieval mechanisms. These mechanisms inject relevant information into the model’s context, aiming to ground the text generation process in factual reality.

The central theme of the paper is the investigation of how planning capabilities inherent in instruction-tuned LLMs can be leveraged to enhance retrieval processes. By incorporating explicit planning stages, the hypothesis is that the retrieval and subsequent grounding of generation processes can be significantly improved, thus reducing hallucination rates. The paper's empirical analysis spans both well-documented entities and events as well as those less documented, providing a broad evaluation of the proposed methodology.

Methodology

The methodology is structured around several core steps:

1. Query-based Retrieval: Initial retrieval involves querying a search engine with the entity name to gather a base set of context documents.
2. Planning: The retrieved documents inform an LLM-based plan generation process, which outlines the segments of the final text.
3. Refined Retrieval: Specific search queries are generated for each segment outlined in the plan, allowing the retrieval of fine-grained, relevant documents.
4. Generation with Contextual Evidence: The final text generation is conditioned on a combined context of initial retrieval results, refined search results, and the initial plan.

Two variants of this approach (Plan-based Retrieval Var.A and Var.B) are evaluated:

• Var.A: Direct inclusion of documents in the model’s context.
• Var.B: Further refined by using questions generated from the initial plan which are answered using an auxiliary question-answering model.

Empirical Evaluation

The models are evaluated using multiple datasets, including biographical and event-specific tasks, along with a set of metrics, most notably the Attributable to Identified Sources (AIS) metric. This metric assesses whether generated sentences can be attributed to any of the context documents.

Key Findings

1. Baseline Performance: Models without retrieval (i.e., relying solely on their parametric knowledge) perform poorly in terms of generating attributable sentences, as indicated by very low AIS scores.
2. Effectiveness of Single Round Retrieval: Introducing retrieval (One-Retrieval) substantially improves attribution rates, validating the basic premise that external context mitigates hallucination.
3. Advantage of Plan-based Retrieval: The proposed plan-based retrieval models outperform simple retrieval mechanisms (One-Retrieval) in most settings. Plan-based methods achieve higher AIS scores, indicating better groundedness in generated text.
4. Importance of Plan Granularity and Refinement: Empirical results show that generating detailed search queries based on fine-grained plans significantly enhances retrieval quality, which translates into more attributable generations.

Discussion and Implications

The research underscores the importance of structured planning in the retrieval-augmented generation paradigm. By explicitly guiding the retrieval process through detailed plans, models can gather and utilize more relevant information, thus producing more grounded outputs.

This approach suggests several practical implications:

• Enhanced Retrieval Mechanisms: Search engines and retrieval-based models can be fine-tuned to support the generation of comprehensive queries from segmented plans, improving the relevance and quality of retrieved documents.
• Reduction in Hallucinations: Systems employing retrieval-augmented LLMs could achieve higher factual accuracy, particularly valuable in domains where precision is crucial, such as legal documents, medical information systems, and educational content.

Future Directions

Future research could explore optimizing the multi-step retrieval and planning mechanism to reduce computational overhead. Additionally, extending this approach to other LLM architectures and domains could validate the generalizability and robustness of the plan-based retrieval approach. Further fine-tuning model architectures to better handle structured plans and integrate retrieved information seamlessly could yield even more significant improvements in grounding and factuality in generated text.

In conclusion, the paper presents a comprehensive methodology leveraging the intrinsic planning abilities of LLMs to augment retrieval processes, which in turn enhance the factual foundation of generated text. This contribution represents a step forward in addressing the persistent challenge of hallucinations in language generation models.