By
Ritik Sharma
February 11, 2024
•
2
min read
As a researcher exploring new techniques in machine learning, you likely aim to push boundaries in developing systems that learn and adapt. The DEMONSTRATE–SEARCH–PREDICT (DSP) framework offers an intriguing approach to in-context learning that merits your attention. Diving into the details of this method, you will discover how it centres on an agent interacting with an environment to gain new knowledge. Weighing the demonstrated strengths of DSP, you may envision fresh applications that can extract greater value from available data. Progress awaits in mapping uncharted territory through this framework.
In the evolving landscape of artificial intelligence, Stanford University has been at the forefront and has introduced a groundbreaking approach that harmonizes the capabilities of frozen language models (LMs) with retrieval models (RMs) for complex natural language processing (NLP) tasks. This innovative technique, dubbed Retrieval Augmented In-Context Learning (RAICL), transcends the traditional boundaries of machine learning by eliminating the need for fine-tuning models, instead utilizing the generative prowess of LMs to dynamically generate synthetic training data within specific knowledge domains. Central to this advancement is the DEMONSTRATE–SEARCH–PREDICT (DSP) methodology, a tripartite process that encompasses the generation of task-guiding examples (DEMONSTRATE), the retrieval of pertinent data (SEARCH), and the synthesis of this information into coherent and contextually relevant responses (PREDICT). This strategic framework not only enables multi-hop searches and sophisticated reasoning but also facilitates a self-optimizing system capable of refining its proficiency over time. Representing a significant paradigm shift in AI, Stanford’s development of DSP marks the transition from static, template-based architectures to dynamic, self-enhancing, graph-based systems, promising unprecedented improvements in handling knowledge-intensive tasks with relative gains of 37–120%, 8–39%, and 80–290% against existing benchmarks.
Introducing the DEMONSTRATE-SEARCH-PREDICT Framework
The DEMONSTRATE-SEARCH-PREDICT (DSP) framework relies on passing natural language texts between a language model (LM) and a retrieval model (RM) in sophisticated pipelines. DSP can express high-level programs that bootstrap demonstrations, search for relevant passages, and generate grounded predictions.
DSP breaks down problems into small transformations that the LM and RM can handle reliably. For example, to answer a question about how many storeys are in a castle, DSP would:
DSP establishes new state-of-the-art results in open-domain, multi-hop, and conversational settings. It delivers substantial gains over a standard retrieve-then-read pipeline and a self-ask pipeline.
DSP allows building complex pipelines without intermediate labels. If the LM and RM can process transformations accurately for one or two examples, these examples become demonstrations. DSP replaces backpropagation with simulating program behavior and programmatically learning from errors using end-task labels and frozen models.
Without hand-labeling each transformation, developers can modify the program’s strategy, swap the training domain, update examples, and use DSP to automatically populate demonstrations. This high modularity facilitates exploring strategies that challenge traditional retrieval-augmented NLP.
DSP gathers passages to support LM transformations, assuming a large, divided knowledge corpus. In simple cases, SEARCH queries the RM for top passages matching the input. More complex strategies manipulate demonstrations and transformations, select subsets of examples, and bootstrap to explore strategies without custom annotations.
DSP helps maximize the value of specialized pre-trained components, enabling participation in developing AI systems and rapidly prototyping new domain systems. The central contribution of DSP is revealing the large space of possibilities for in-context learning.
The DEMONSTRATE–SEARCH–PREDICT (DSP) framework facilitates in-context learning by enabling the composition of language models (LMs) and retrieval models (RMs) in sophisticated pipelines. Rather than posing end-task prompts directly to LMs, DSP expresses in-context learning strategies as deliberate programs that bootstrap demonstrations, search for relevant passages, and generate grounded predictions.
Composing LMs and RMs
DSP combines LMs and RMs through natural language interactions in flexible pipelines. It consists of simple, composable functions for implementing in-context learning systems as programs that solve knowledge-intensive tasks. These programs can rapidly prototype systems for new domains and maximize the value of specialized pretrained components.
Bootstrapping Annotations for Pipelines
DSP programs can automatically annotate demonstrations for complex pipelines using weak supervision from end-task labels. The SEARCH stage generates a query, and the RM retrieves a relevant passage. A second search finds additional information, which the PREDICT stage provides to the LM with the passages to generate an answer. Although this multi-hop program implements behaviors like query generation, it requires no hand-labeled examples of the intermediate queries and passages. Instead, the DEMONSTRATE stage uses the end-task label alone to bootstrap examples for the full pipeline.
Iteratively Decomposing Complex Queries
DSP enables strategies like iteratively decomposing complex queries into a series of small transformations that LMs and RMs can handle more reliably.DSP programs for question answering implement novel, reusable transformations such as rewriting questions to resolve conversational dependencies and summarizing the results of intermediate hops. By breaking down complex questions into a series of simple queries, DSP elicits grounded responses from the LM.
In summary, the DSP framework reveals conceptual possibilities for in-context learning by composing LMs and RMs in flexible pipelines. DSP expresses in-context learning strategies as deliberate programs, spawning capabilities like bootstrapping annotations for pipelines and iteratively decomposing complex queries. For knowledge-intensive tasks, DSP programs can implement sophisticated strategies to achieve state-of-the-art results in in-context learning.
Demonstrate
The Demonstrate stage utilizes the language model (LM) to generate natural language demonstrations that solve the given task. These demonstrations are used to automatically annotate the training data to bootstrap the learning process. The LM is prompted with an end-task label, like an answer span, and generates a demonstration, like a natural language explanation, that results in that label.
For example, to demonstrate how to answer the question “When was the discoverer of Palomar 4 born?” the LM might generate the following demonstration:
The demonstrations are used to automatically annotate the training data by extracting the intermediate steps, like looking up the discoverer and their birth date. These annotations are then used to train the system.
Search
The Search stage utilizes the retrieval model (RM) to search for and retrieve relevant passages of information based on the current context. The search queries are often generated by the LM in the Demonstrate or Predict stages. The retrieved passages are then incorporated into the context and fed back into the LM.
For example, to find the discoverer of Palomar 4, the LM might generate the query “Who discovered Palomar 4?” The RM would search and retrieve a passage stating that Henri Chretien discovered Palomar 4. This passage is then added to the context and used to continue the demonstration.
Predict
The Predict stage uses the LM to generate an end-task prediction based on the current context, which has been augmented with retrieved passages from the Search stage. The LM is prompted with the context and generates a prediction, like an answer span.
For example, after searching for and finding passages on the discoverer of Palomar 4 and their birth date, the LM can predict that the answer to the original question “When was the discoverer of Palomar 4 born?” is 1889.
The power of the DSP framework comes from composing multiple instances of these stages into complex, multi-step pipelines tailored for a given task. The stages work together, with the output of one stage becoming the input to the next, to solve complex, knowledge-intensive problems.
The DSP framework has broad applications for knowledge-intensive natural language processing. Some potential real-world use cases include:
•Question answering systems — DSP can be used to build conversational agents and chatbots capable of multi-turn question answering. The SEARCH component finds relevant information to answer user questions, while PREDICT generates a response. The ability to compose complex question-answering pipelines makes DSP well-suited for this task.
•Information extraction — DSP could be leveraged to build systems that extract structured data from unstructured text. The DEMONSTRATE stage provides examples to learn extraction patterns, SEARCH finds additional examples, and PREDICT extracts information from new input.
•Summarization — DSP presents an opportunity to develop unsupervised or weakly supervised summarization systems. The framework could generate extractive summaries by searching for and extracting the most important sentences from a document. Or, it could produce abstractive summaries by paraphrasing and condensing the key ideas.
•Recommendation systems — Recommendation engines suggest items to users based on their interests and past behavior. DSP could be adapted to build a content-based recommender system by learning user preferences from examples (DEMONSTRATE), finding related items (SEARCH), and generating recommendations (PREDICT).
The flexibility and modularity of the DSP framework make it well-suited for many knowledge-intensive NLP tasks. While still an emerging area of research, DSP shows promise for building systems that can understand language, reason over knowledge, and generate intelligent responses — all without the massive datasets required to train end-to-end neural models. DSP presents an opportunity to make progress on ambitious NLP problems using the knowledge and skills we already have.
To implement the DSP framework effectively, there are several best practices to keep in mind:
Choose a frozen language model and retrieval model suited to your task. Select models that have been pretrained on large datasets relevant to your domain. For open-domain question answering, models like GPT-3 and ColBERT are good options.
Design your DSP program deliberately. Map out the high-level strategy for composing the LM and RM to solve your task. This could involve bootstrapping annotations, rewriting inputs, generating grounded responses, and more. Think about how to break down complex problems into small, reliable transformations.
Bootstrap your program with weak supervision. Use a small set of examples to generate initial demonstrations that the LM can then build upon. This helps the LM learn the overall shape and purpose of the program.
Summarize and rewrite at intermediate hops. For multi-hop reasoning, summarize information at each hop to generate a coherent rewritten input for the next hop. This makes the problem more tractable for the LM and RM.
Generate self-consistent responses. When aggregating information from multiple passages, generate a response that is consistent with facts and details across all passages. This results in more grounded, logically coherent predictions.
Evaluate and iterate. Evaluate your DSP program to identify areas for improvement. Make changes to the high-level strategy, add or remove transformations, redesign demonstrations, and retrain as needed. Iterating on program design is key to achieving the best results.
The DEMONSTRATE–SEARCH–PREDICT (DSP) framework revolutionizes in-context learning by enhancing the synergy between language models (LMs) and retrieval models (RMs), offering a novel approach to tackle knowledge-intensive tasks efficiently. DSP’s unique methodology, comprising demonstration, information retrieval, and predictive synthesis, enables deeper cognitive engagement and personalized learning paths. Its success lies in its systematic fusion of advanced NLP techniques, fostering more intricate LM and RM interactions.
DSP accelerates AI system development across new domains without the need for fine-tuning, thanks to its modular design and natural language programming capability. This lowers development barriers and invites wider participation. Key to DSP is its ability to support task-aware programming, automate the annotation of complex demonstrations, and establish new performance benchmarks, all while sidestepping the extensive domain expertise traditionally required.
Representing a breakthrough in AI, DSP introduces a transformative paradigm for creating sophisticated systems from pre-trained models through natural language, showing remarkable empirical progress across NLP tasks and promising a rich avenue for future innovation and in-context learning exploration.
References:
https://arxiv.org/abs/2212.14024https://arxiv.org/abs/2310.03714https://github.com/stanfordnlp/dspy
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