DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints

Language models (LMs) are becoming integral to applications such as conversational agents and writing assistants.

Despite their utility, the probabilistic nature of LMs often leads to outputs that fail to meet domain-specific constraints or the requirements of the larger pipeline.

To address these issues, various techniques have been explored, including constrained decoding, self-reflection, tree search, domain-specific languages like LMQL, and the use of assertions and guardrails.

Other frameworks offer developers interfaces to build complex LM pipelines and exert some control over outputs. However, these frameworks typically fall short in enforcing arbitrary computational constraints or enabling introspective self-refinement without extensive prompt engineering.

The integration of LM calls as composable modules is fostering a new programming paradigm.

This paper introduces LM Assertions, a programming construct designed to specify computational constraints that LMs must satisfy.

These constructs are seamlessly integrated into the DSPy programming model for LMs, presenting new strategies for compiling programs with LM Assertions into more reliable and accurate systems.

The study includes four case studies in text generation, demonstrating that LM Assertions not only enhance compliance with imposed rules but also improve downstream task performance. The results show up to 164% higher compliance with constraints and up to 37% higher-quality responses, underscoring the effectiveness of LM Assertions in creating robust and high-performing LM pipelines.

DSPy Assertions: Computational Constraints for Self-Refining...arXiv.org

LM Assertions: Enhancing Language Model Pipelines with Computational Constraints

Background and Motivation

The goals of Language Model (LM) Assertions are broad and can be applied to any LM program.

To leverage its modular paradigm, flexibility, and extensibility, this work is implemented as extensions to the DSPy framework (Khattab et al., 2024).

The DSPy framework provides a programming model for building declarative LM pipelines and compiling them into auto-optimized prompt (or finetune) chains.

This paper introduces LM Assertions and demonstrates their usefulness in self-refinement within LM pipelines through a realistic example.

The DSPy Programming Model

DSPy is designed to programmatically solve advanced tasks with language and retrieval models by composing and declaring modules.

The primary goal of DSPy is to replace fragile "prompt engineering" tricks with composable modules and automatic optimizers. Instead of using free-form string prompts, DSPy allows programmers to define a signature that specifies what an LM needs to do in a declarative manner.

Defining Signatures

For instance, a programmer can define a signature for a module that consumes a question and returns an answer:

qa = dspy.Predict("question -> answer")
qa(question="Where is the Eiffel tower?") 
# Output: The Eiffel Tower is located in Paris, France.

To use this signature, the programmer declares a module with it.

The core module for working with signatures in DSPy is Predict.

Internally, it stores the supplied signature and constructs a formatted prompt according to the signature’s inputs and outputs when called. It then calls an LM with a list of demonstrations (if any) following this format.

Composable Modules

DSPy modules, like dspy.Predict, encapsulate prompting techniques, transforming them into modular functions that support any signature. This approach contrasts with manually writing task-specific prompts with tuned instructions or few-shot examples.

For example, a DSPy module implementing the "chain-of-thought" prompting technique might look like this:

class ChainOfThought(dspy.Module):
    def __init__(self, signature):
        rationale_field = dspy.OutputField(prefix="Reasoning: Think step by step.")
        signature = dspy.Signature(signature).prepend_output_field(rationale_field)
        self.predict = dspy.Predict(signature)

    def forward(self, **kwargs):
        return self.predict(**kwargs)

DSPy modules can be composed into arbitrary pipelines by declaring the necessary modules at initialization and then using arbitrary code to call the modules in a forward method.

Optimizers and Compilation

DSPy provides optimizers to automate generating high-quality demonstrations (few-shot examples) or instructions for a task based on a given metric. The process of selecting few-shot examples can be considered compiling the LM pipeline application.

Challenges and LM Assertions

Despite the strengths of DSPy, there are challenges.

DSPy signatures provide type hints that softly shape an LM’s behavior, but the framework lacks constructs for specifying arbitrary computational constraints that the pipeline must satisfy. Additionally, there is a need for mechanisms that allow the LM pipeline to refine its outputs and respect these constraints at compile time.

To address these challenges, LM Assertions are integrated as first-class primitives in DSPy. Similar to Pythonic assertions, they allow DSPy to constrain LM outputs.

These assertions can be:

• Strict Restrictions: Enforcing exact compliance.

• Softer Guidelines: Allowing for backtracking and self-correction.

• Debugging Statements: Providing insights during development.

Motivating Example: Multi-Hop Question Answering

Consider a multi-hop question-answering program using LM Assertions:

1. Defining Assertions:

   • Assertions can ensure intermediate steps meet certain criteria.

   • They help refine outputs and guide self-correction.

2. Implementation:

   • Use DSPy’s modular and declarative approach.

   • Integrate LM Assertions for computational constraints.

Example Usage of LM Assertions

In a realistic example, LM Assertions can be employed to ensure the correctness and reliability of multi-hop question-answering by enforcing constraints at each step of the reasoning process. This helps in refining the outputs and maintaining the integrity of the LM pipeline.

Semantics of LM Assertions

LM Assertions are introduced to enhance the reliability, predictability, and correctness of language model (LM) pipelines by dictating certain conditions or rules that must be adhered to during execution. These assertions are integrated into DSPy to provide computational constraints that guide and refine the LM pipeline’s behavior. LM Assertions are categorized into two constructs: Assert and Suggest.

LM Assertions Constructs

1. Assert:

   • Purpose: Enforces strict constraints that must be met.

   • Mechanism: When an assertion fails, the pipeline enters a retry state, allowing it to reattempt the failing LM call while being aware of previous attempts and error messages. If the assertion continues to fail after a maximum number of retries, the pipeline transitions to an error state and raises an AssertionError, terminating execution.

2. Suggest:

   • Purpose: Provides non-binding guidance that suggests but does not enforce conditions.

   • Mechanism: Similar to Assert, if a suggestion fails, the pipeline enters a retry state. However, if the suggestion continues to fail after the maximum retries, the pipeline logs a warning (SuggestionError) and continues execution, allowing for flexibility and resilience to suboptimal states.

Difference from Conventional Assertions

• Conventional Assertions: Typically used in most programming languages as a debugging aid to check a condition and raise an error if the condition fails, usually terminating the program.

• Assert in DSPy: Offers a sophisticated retry mechanism, enabling retries and adjustments before concluding an error is irrecoverable, making it more powerful than conventional assertions.

Formal Semantics of LM Assertions

Semantics of Assert

The Assert construct enforces invariants within the LM pipeline using a state transition system adapted from big-step operational semantics.

Here’s how it works:

• State Representation: The pipeline’s state is denoted by ( \sigma_r ), where ( r ) is the current retry count.

• Transition Relation: ( \sigma_r \vdash i \rightarrow \sigma' ) means that under state ( \sigma_r ), instruction ( i ) transitions the state to ( \sigma' ).

• Maximum Retries: Denoted by ( R ).

Simplified Semantics:

• If the expression ( e ) evaluates to true, the pipeline transitions to a new state ( \sigma' ) and continues execution.

• If ( e ) evaluates to false and the retry count ( r ) is less than ( R ), the pipeline transitions to a retry state ( \sigma_{r+1} ), incrementing the retry count.

• If ( e ) evaluates to false and ( r ) is greater than or equal to ( R ), the pipeline transitions to an error state ( \sigma_{\perp} ), raising an AssertionError and halting execution.

Semantics of Suggest

The Suggest construct provides non-binding guidance to the LM pipeline. Its semantics are similar to Assert but with key differences:

Simplified Semantics:

• If the expression ( e ) evaluates to true, the pipeline transitions to a new state ( \sigma' ) and continues execution.

• If ( e ) evaluates to false and the retry count ( r ) is less than ( R ), the pipeline transitions to a retry state ( \sigma_{r+1} ), attempting recovery.

• If ( e ) evaluates to false and ( r ) reaches ( R ), the pipeline transitions to a new state ( \sigma'' ), logs the message ( m ) as a SuggestionError warning, resets the retry count, and continues execution.

Analysis of "Assertion-Driven Optimizations"

The section on "Assertion-Driven Optimizations" outlines three key mechanisms that leverage LM Assertions to enhance the performance and reliability of LM pipelines: Assertion-Driven Backtracking, Assertion-Driven Example Bootstrapping, and Counterexample Bootstrapping.

Assertion-Driven Backtracking

Concept:

• Both Assert and Suggest constructs allow the pipeline to retry a failing LM call, enabling self-refinement in a retry state.

• The control flow of the LM pipeline is dynamically altered during execution, depending on whether the assertions pass or fail.

• When assertions pass, the control flow moves to the next module. On failure, an error handler determines the next instruction based on the current state and error message.

• If the maximum retry attempts are not surpassed, the handler retries the failing LM module with an updated prompt, incorporating past failing outputs and instructions.

• If the retries exceed the maximum, execution halts for Assert or moves to the next module for Suggest.

Implementation:

• The constructs and handlers are implemented in DSPy, making the framework more robust and adaptable.

Benefits:

• Provides a mechanism for dynamic error handling and control flow adjustment.

• Enhances the pipeline’s ability to self-correct and refine outputs, improving overall reliability and correctness.

Assertion-Driven Example Bootstrapping

Concept:

• LM Assertions can optimize prompts through the BootstrapFewShot optimizer in DSPy, which employs a teacher-student method.

• The teacher model bootstraps representative few-shot demonstrations for the student model.

• Assertions ensure that not only the final response but also the intermediate module outputs are correct, avoiding incorrect demos for intermediate modules.

Implementation:

• Applies assertion-driven backtracking to the teacher model in the BootstrapFewShot optimizer, ensuring all bootstrapped demonstrations meet intermediate constraints.

Benefits:

• Produces high-quality examples for all intermediate modules, not just the final output.

• Ensures that prompts adhere to constraints, leading to more accurate and reliable LM pipelines.

Counterexample Bootstrapping

Concept:

• Integrates LM Assertions and assertion-driven backtracking in the teacher model to collect traces where the language model fails certain assertions.

• The optimizer uses these erroneous examples (counterexamples) during backtracking as demonstrations.

• Counterexamples serve as negative demonstrations, guiding models to avoid similar mistakes.

• Demonstrations of fixing particular LM Assertion failures help the student model pass underlying constraints more effectively.

Implementation:

• Incorporates feedback from erroneous examples and provides demonstrations for fixing assertion failures.

Benefits:

• Eliminates the overhead of backtracking and self-refinement for the student model.

• Enhances the model’s ability to generate responses that adhere to programmer-defined assertions, even without direct use of LM Assertions and backtracking.

Overall Benefits

• Enhanced Reliability: LM Assertions ensure that LM pipelines adhere to defined constraints, leading to more predictable and reliable outputs.

• Improved Correctness: By enforcing intermediate constraints and refining outputs through retries, the overall correctness of the pipeline is significantly improved.

• Higher Quality Examples: Assertion-driven example bootstrapping ensures that all examples, including intermediate steps, meet the desired constraints, leading to better training and performance of the student model.

• Efficient Error Handling: Counterexample bootstrapping and dynamic control flow adjustments provide efficient mechanisms for error handling and correction, minimizing the impact of errors and enhancing robustness.

Conclusion

The assertion-driven optimizations in DSPy leverage LM Assertions to create a more robust, reliable, and correct LM pipeline. By enabling dynamic error handling, refining outputs, and ensuring high-quality examples, these mechanisms significantly enhance the performance and reliability of language model pipelines. The implementation of these constructs in DSPy showcases the framework’s adaptability and extensibility in addressing complex LM challenges.