PARADISE: Evaluating Language Models' Planning Skills with Procedural Text

Core Concepts

The author presents PARADISE, a task evaluating language models' planning abilities using procedural text. The study reveals insights into model behavior and performance compared to human capabilities.

Abstract

The content discusses the creation of PARADISE, an abductive reasoning task using procedural text from wikiHow to evaluate language models' planning skills. The study highlights the effectiveness of task-specific small models over large language models in most scenarios. Despite advancements, all models fall short of human performance. The analysis uncovers intriguing insights such as variations in model behavior with dropped keywords and struggles with different types of goals.

The dataset contains warnings and tips associated with goals, excluding intermediary steps, to test the ability of models to infer implicit knowledge solely from the given goal. Experiments reveal that fine-tuned small models tailored to specific tasks are more effective than zero-shot prompting across all LLMs, including GPT-4. However, all models still lag behind human performance.

The study also delves into research questions such as whether models perform well due to simple keyword matching, if different model families fail on different instances, and how performance compares for explicit versus implicit warnings/tips. Additionally, it explores reverse inference tasks and the transfer learning impact of proposed tasks on out-of-domain procedural tasks.

Stats

Majority of these studies use toy simulation environments like ALFRED (Shridhar et al., 2020), BlocksWorld, and VirtualHome (Puig et al., 2018) which have little lexical and domain variance.
The PARADISE dataset contains +104K warnings and tips in total.
Mistral 7B is an open-source LLM that outperforms Vicuna 33B and LLaMA-2 70B in both tasks.
DeBERTa performs best among fine-tuned PLMs but still falls behind human performance.
GPT-4 is the best-performing LLM among proprietary LLMs.

Quotes

"Despite advancements, all models fall short of human performance."
"Our experiments address a broad range of research questions."
"The PARADISE dataset offers valuable insights into language model behavior."

Key Insights Distilled From

PARADISE

by Arda... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.03167.pdf

Deeper Inquiries

Do language models struggle with physical goals compared to abstract ones?

In the context of the PARADISE dataset and evaluation, it was observed that different model families exhibit distinct failure patterns when dealing with various types of goals. Specifically, DeBERTa struggled more with tangible, physical, and craft-related goals, while GPT-4 encountered challenges with abstract, digital, and social objectives. This indicates that there might be a tendency for language models to face difficulties in reasoning about physical goals compared to abstract ones.

Are there ethical considerations regarding the proprietary nature of some LLMs?

The proprietary nature of some Large Language Models (LLMs) raises important ethical considerations related to transparency, accountability, bias mitigation, and fairness. When LLMs are not open-source or have limited accessibility due to being proprietary software controlled by specific entities or organizations, concerns arise about algorithmic transparency and potential biases embedded in these models. Additionally, issues around data privacy and ownership can also come into play when using proprietary LLMs for sensitive tasks.

How can implicit reasoning skills be further developed in language models?

To enhance implicit reasoning skills in language models like those evaluated in the PARADISE dataset:

Diverse Training Data: Expose models to a wide range of text genres containing implicit relationships.
Fine-tuning Strategies: Tailor fine-tuning approaches specifically for abductive reasoning tasks focusing on missing information.
Multi-Task Learning: Incorporate multiple tasks requiring different levels of explicitness to encourage nuanced understanding.
Adversarial Training: Introduce adversarial examples challenging the model's ability to infer implicit knowledge.
Human Feedback Loop: Implement mechanisms where human feedback corrects model errors related to implicit reasoning during training iterations.

By implementing these strategies along with continuous evaluation and refinement based on real-world applications across diverse domains will contribute towards enhancing the implicit reasoning capabilities of language models significantly over time.