Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2212.14024v2)

Abstract: Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen LLMs (LM) and retrieval models (RM). Existing work has combined these in simple "retrieve-then-read" pipelines in which the RM retrieves passages that are inserted into the LM prompt. To begin to fully realize the potential of frozen LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware demonstrations, search for relevant passages, and generate grounded predictions, systematically breaking down problems into small transformations that the LM and RM can handle more reliably. We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings, establishing in early evaluations new state-of-the-art in-context learning results and delivering 37-120%, 8-39%, and 80-290% relative gains against the vanilla LM (GPT-3.5), a standard retrieve-then-read pipeline, and a contemporaneous self-ask pipeline, respectively. We release DSP at https://github.com/stanfordnlp/dsp

• The paper presents the DSP framework that integrates demonstration, search, and prediction to enhance performance in complex NLP tasks.
• It employs a modular, Python-based approach that delivers significant empirical gains on datasets like Open-SQuAD, HotPotQA, and QReCC.
• The framework reduces annotation costs and promotes scalable AI by treating language and retrieval models as reusable infrastructure.

A Professional Overview of "Demonstrate–Search–Predict: Composing Retrieval and LLMs for Knowledge-Intensive NLP"

In this paper, the authors explore the integration of retrieval models (RMs) and LLMs (LMs) to enhance performance in knowledge-intensive NLP tasks. They introduce the Demonstrate–Search–Predict (DSP) framework, which allows for sophisticated interplay between these models, moving beyond the conventional "retrieve-then-read" approach.

Core Contributions

The primary focus of this work is to demonstrate how LMs and RMs can be combined using the DSP framework. This involves a three-stage process:

1. Demonstrate: This phase conditions the LM to interpret training examples effectively, bootstrapping additional information pertinent to pipeline processing.
2. Search: The RM is utilized to gather relevant information from large corpora, thereby equipping the LM with concrete data for decision-making.
3. Predict: Finally, the LM uses the gathered information to generate well-founded predictions.

The authors argue that DSP capitalizes on the capabilities of existing LMs and RMs, leading to more grounded AI systems with reduced development and annotation costs. By treating these models as infrastructures, they pave the way for deploying advanced NLP systems across various domains.

Methodology

The DSP framework leverages simple Python-based programs to demonstrate the utility in contexts such as open-domain question answering (QA), multi-hop reasoning, and conversational AI. Each component within the DSP framework performs specific tasks contributory to the overall system’s accuracy and reliability:

• Demonstrate involves creating contexts from existing examples, potentially improving the LM’s ability to handle complex queries.
• Search includes iterative retrieval processes, enabling the system to sift through a vast amount of data to find pertinent information.
• Predict uses aggregation techniques like self-consistency to produce final outputs, colloquially known as answers, responses, or solutions.

Results and Discussions

The evaluation on datasets such as Open-SQuAD, HotPotQA, and QReCC shows DSP’s capacity to outperform existing models by a substantial margin. DSP demonstrated relative gains in accuracy and efficacy compared to vanilla LMs and the retrieve-then-read paradigm. Notably, the DSP framework achieved significant empirical gains against contemporaneous methods like self-ask, highlighting its versatility and robustness.

The results emphasized DSP's ability to perform iterative information retrieval and reasoning, particularly in multi-hop scenarios, affirming its potential to facilitate more nuanced and reliable AI interactions. The framework’s composability also showed promise in facilitating domain adaptation and allowing researchers to explore multitudinous retrieval and prediction strategies without custom annotations on intermediate processes.

Implications and Future Directions

The authors suggest that DSP not only addresses immediate challenges in model composability and adaptability but also lays the groundwork for future advancements in AI systems' design. As AI steadily adopts knowledge-intensive applications, DSP may set a precedent for incorporating diverse modules in a structured manner. The notion of treating LMs and RMs as reusable infrastructure components heralds a shift towards more modular, scalable AI architectures.

For future work, the exploration of additional domains and further refinement of DSP functionalities could provide deeper insights into potential applications. Investigating DSP’s effectiveness with larger, more diverse datasets, or considering alternative architectures might also yield new avenues of innovation.

In summary, this paper offers a substantial contribution to the field of NLP by presenting the DSP framework as a sophisticated method for composing LMs and RMs, achieving impressive outcomes in knowledge-intensive tasks.