Evaluating Compositional and Conditional Reasoning Capabilities of Language Models in a Flight Booking Task

Core Concepts

Contemporary large language models struggle to effectively handle complex conditional and compositional reasoning required for aligning detailed user preferences with available flight options.

Abstract

This paper introduces GroundCocoa, a benchmark for evaluating the compositional and conditional reasoning capabilities of language models in the context of a flight booking task. The key highlights are: GroundCocoa consists of natural language user requirements that vary in their conditional and compositional complexity. These requirements are grounded to a set of flight options, creating a multiple-choice task. Experiments on several state-of-the-art language models, including GPT-4 Turbo, reveal a significant performance gap, with even the best model achieving only 67% accuracy. This underscores the challenges posed by conditional and compositional reasoning. The authors analyze the impact of increasing complexity, measured by factors like reasoning width and conditional dependencies. They find that model performance degrades rapidly as complexity increases. The authors also assess model robustness to unconventional user requirements, observing a notable drop in performance for the top-performing models, indicating a pretraining bias towards more typical needs. The authors introduce entropy as a metric to quantify the confusion caused by conditional constraints in the user query, providing insights into why models may succeed or fail on certain samples. Overall, the GroundCocoa benchmark highlights the limitations of current language models in handling the nuanced reasoning required for real-world task grounding, and calls for further advancements in compositional and conditional reasoning capabilities.

Stats

The price of the flight should be more than $1800. The carbon emission of the flight should be above the average for that route. The number of layovers on the route should be greater than 2.

Quotes

"Conditional reasoning involves the comprehension and application of logical rules that are typically structured in "if-then" formats. It is critical to personal decision-making in everyday life through an evaluation of potential scenarios and anticipation of consequences." "Compositional reasoning, on the other hand, is the ability to combine solutions to simpler sub-problems, and integrate them in a structured manner to solve a more complex task. This cognitive process entails understanding the interplay between different sub-problems."

Key Insights Distilled From

Cleared for Takeoff? Compositional & Conditional Reasoning may be the Achilles Heel to (Flight-Booking) Language Agents

by Harsh Kohli,... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04237.pdf

Deeper Inquiries

How can language models be trained to better handle unconventional user requirements and preferences that deviate from typical patterns in the training data?

In order to enhance the ability of language models to handle unconventional user requirements, several strategies can be implemented during the training process: Diverse Training Data: Including a wide range of examples during training that cover various scenarios and edge cases can help the model learn to generalize better to unconventional user preferences. Data Augmentation: Introducing variations in the training data through techniques like paraphrasing, adding noise, or altering the context can expose the model to different forms of user requirements, making it more adaptable to diverse inputs. Fine-Tuning on Atypical Data: After pretraining on standard datasets, fine-tuning the model on a specialized dataset containing atypical queries can help it learn to handle unconventional user preferences more effectively. Prompt Engineering: Crafting prompts that explicitly guide the model to pay attention to specific aspects of the input related to unconventional requirements can improve its performance in such scenarios. Regularization Techniques: Applying regularization methods such as dropout, weight decay, or early stopping can prevent overfitting to common patterns in the training data, allowing the model to better accommodate deviations in user preferences. By incorporating these strategies into the training pipeline, language models can be trained to exhibit greater flexibility and adaptability when faced with unconventional user requirements.

How can language models be trained to better handle unconventional user requirements and preferences that deviate from typical patterns in the training data?

In order to enhance the ability of language models to handle unconventional user requirements, several strategies can be implemented during the training process: Diverse Training Data: Including a wide range of examples during training that cover various scenarios and edge cases can help the model learn to generalize better to unconventional user preferences. Data Augmentation: Introducing variations in the training data through techniques like paraphrasing, adding noise, or altering the context can expose the model to different forms of user requirements, making it more adaptable to diverse inputs. Fine-Tuning on Atypical Data: After pretraining on standard datasets, fine-tuning the model on a specialized dataset containing atypical queries can help it learn to handle unconventional user preferences more effectively. Prompt Engineering: Crafting prompts that explicitly guide the model to pay attention to specific aspects of the input related to unconventional requirements can improve its performance in such scenarios. Regularization Techniques: Applying regularization methods such as dropout, weight decay, or early stopping can prevent overfitting to common patterns in the training data, allowing the model to better accommodate deviations in user preferences. By incorporating these strategies into the training pipeline, language models can be trained to exhibit greater flexibility and adaptability when faced with unconventional user requirements.

How can language models be trained to better handle unconventional user requirements and preferences that deviate from typical patterns in the training data?

In order to enhance the ability of language models to handle unconventional user requirements, several strategies can be implemented during the training process: Diverse Training Data: Including a wide range of examples during training that cover various scenarios and edge cases can help the model learn to generalize better to unconventional user preferences. Data Augmentation: Introducing variations in the training data through techniques like paraphrasing, adding noise, or altering the context can expose the model to different forms of user requirements, making it more adaptable to diverse inputs. Fine-Tuning on Atypical Data: After pretraining on standard datasets, fine-tuning the model on a specialized dataset containing atypical queries can help it learn to handle unconventional user preferences more effectively. Prompt Engineering: Crafting prompts that explicitly guide the model to pay attention to specific aspects of the input related to unconventional requirements can improve its performance in such scenarios. Regularization Techniques: Applying regularization methods such as dropout, weight decay, or early stopping can prevent overfitting to common patterns in the training data, allowing the model to better accommodate deviations in user preferences. By incorporating these strategies into the training pipeline, language models can be trained to exhibit greater flexibility and adaptability when faced with unconventional user requirements.