Analogical Reasoning on Narratives: A Comprehensive Computational Framework and Benchmark

核心概念

State-of-the-art large language models struggle to detect system analogies between narratives, especially in cross-domain (far) analogies, highlighting the need for more advanced computational analogical reasoners.

摘要

The paper presents a comprehensive computational framework for analogical reasoning on narratives, operationalizing key concepts from cognitive theories of analogy. The framework extracts narrative elements (characters, relationships, actions, goals, locations, proverbs), forms surface and system mappings between narratives, and defines four categories of analogies and disanalogies based on the interplay between these mappings.
The authors leverage this framework to create the Analogical Reasoning on Narratives (ARN) benchmark, containing 1.1k triples of query narratives, analogies, and distractors. Experiments on ARN show that while state-of-the-art large language models (LLMs) can largely recognize near analogies, they struggle with far analogies in a zero-shot setting, with GPT4.0 scoring below random. Providing solved examples and chain-of-thought reasoning enhances the models' analogical reasoning ability, but even the best model only performs halfway between random and human performance, highlighting the need for more advanced computational analogical reasoners.

統計資料

LLMs struggle to detect far analogies, with GPT4.0 scoring below random in the zero-shot setting.
Providing solved examples and chain-of-thought reasoning improves performance, but the best model still only performs halfway between random and human.
The distraction effect of surface mappings based on actions is the strongest for most LLMs.

引述

"As a core cognitive skill that enables the transferability of information across domains, analogical reasoning has been extensively studied for both humans and computational models."
"Analogical reasoning is a core cognitive skill unique to humans (Penn et al., 2008; Hofstadter, 2001), defined as the ability to perceive and utilize the similarities between situations or events based on (systems of) relations rather than surface similarities."
"Shifting toward more complex settings, narrative-based analogy benchmarks that involve system mappings rather than simple word-based relational mappings, and are aligned with cognitive theories have been rarely considered."

從以下內容提煉的關鍵洞見

ARN: Analogical Reasoning on Narratives

by Zhivar Soura... 於 arxiv.org 04-24-2024

https://arxiv.org/pdf/2310.00996.pdf

深入探究

How can the computational framework and benchmark be extended to handle longer and more complex narratives?

To extend the computational framework and benchmark to handle longer and more complex narratives, several strategies can be implemented:

Enhanced Information Extraction: Develop more advanced techniques for extracting narrative elements from longer texts. This may involve utilizing more sophisticated natural language processing models to accurately identify characters, relationships, actions, goals, locations, and proverbs in lengthy narratives.

Hierarchical Mapping: Implement a hierarchical mapping approach to capture the relationships between narrative elements at different levels of granularity. This can help in representing the complex interplay between characters, actions, and themes in longer narratives.

Contextual Understanding: Incorporate context-aware models that can understand the sequential flow of events in narratives. This can help in establishing more nuanced surface and system mappings between different parts of a longer narrative.

Scalability: Ensure that the framework and benchmark are scalable to handle a larger volume of data and more diverse narrative structures. This may involve optimizing the computational resources and algorithms used in the framework.

Evaluation Metrics: Develop new evaluation metrics that can assess the performance of models on longer and more complex narratives. These metrics should consider the ability of models to capture intricate analogies and reason over extended storylines.

By incorporating these strategies, the computational framework and benchmark can be extended to effectively handle longer and more complex narratives, providing a more comprehensive evaluation of analogical reasoning abilities in computational models.

How can the insights from human analogical reasoning be better incorporated into the design of advanced computational analogical reasoners?

Incorporating insights from human analogical reasoning into the design of advanced computational analogical reasoners can significantly enhance their performance and cognitive abilities. Here are some ways to achieve this:

Narrative Understanding: Develop models that can comprehend narratives at a deeper level, similar to how humans interpret stories. This involves capturing the nuances of character interactions, plot development, and thematic elements to enable more sophisticated analogical reasoning.

Relational Mapping: Emphasize the importance of relational mapping in analogical reasoning, mirroring how humans identify underlying connections between different narratives. Models should be trained to prioritize system mappings over surface similarities for more accurate analogical reasoning.

Abstraction and Generalization: Enable computational reasoners to abstract key concepts from narratives and generalize their learnings to new scenarios. This can be achieved by training models on a diverse range of narratives and encouraging them to apply analogical reasoning across various contexts.

Explainable AI: Implement mechanisms for explaining the reasoning process of computational analogical reasoners, making their decisions transparent and interpretable. This aligns with human cognitive processes and fosters trust in the model's capabilities.

Interactive Learning: Facilitate interactive learning environments where computational models can receive feedback and guidance from human users. This iterative process can help refine the analogical reasoning abilities of the models based on human insights and corrections.

By integrating these strategies, advanced computational analogical reasoners can leverage the cognitive principles of human analogical reasoning to achieve higher levels of performance and cognitive sophistication.

What other narrative elements beyond the ones considered in this study could be leveraged to improve analogical reasoning?

In addition to the narrative elements of characters, relationships, actions, goals, locations, and proverbs, several other narrative elements could be leveraged to enhance analogical reasoning in computational models:

Themes and Motifs: Incorporating themes and motifs present in narratives can provide a deeper understanding of the underlying messages and symbolism, enabling models to draw analogies based on thematic similarities.

Emotions and Sentiments: Analyzing the emotional context of narratives can help in identifying patterns related to character emotions, mood shifts, and overall sentiment, which can contribute to more nuanced analogical reasoning.

Plot Structure: Considering the structural components of a narrative, such as exposition, rising action, climax, and resolution, can aid in recognizing narrative arcs and plot developments for analogical comparisons.

Conflict and Resolution: Focusing on the conflicts faced by characters and how they are resolved can offer insights into the narrative dynamics and the transformation of characters, leading to richer analogical reasoning capabilities.

Symbolism and Imagery: Exploring the symbolic elements and visual imagery used in narratives can provide additional layers of meaning for models to analyze and draw analogies based on symbolic representations.

Narrative Voice and Style: Taking into account the narrative voice, tone, and writing style can help in understanding the author's perspective and narrative techniques, contributing to a more comprehensive analysis for analogical reasoning.

By incorporating these additional narrative elements into the computational framework, models can gain a more holistic understanding of narratives and improve their analogical reasoning abilities across a wider range of storytelling aspects.