Exploring Uncommon Scenarios: A Benchmark for Abductive Reasoning about Unlikely Situations

核心概念

Generating natural language explanations that make uncommon or unlikely outcomes more plausible given a context.

摘要

The paper introduces UNCOMMONSENSE, a new benchmark for evaluating the ability of language models to perform abductive reasoning about uncommon and unlikely situations.
The key highlights are:

UNCOMMONSENSE contains 20,947 context-outcome pairs, where the outcomes are explicitly uncommon or unlikely given the provided contexts. These uncommon outcomes are sourced from incorrect answers in existing commonsense reasoning datasets.

The dataset also includes 41,711 crowd-sourced explanations that rationalize how the uncommon outcomes could plausibly arise from the given contexts. An additional 41,375 explanations are generated by enhancing the crowd-written explanations using a large language model (LLM), and 58,881 explanations are generated directly by the LLM.

The authors analyze the differences between the crowd-written, LLM-generated, and LLM-enhanced explanations. They find that while LLM-generated explanations are more specific, the crowd-written explanations are more diverse and better at connecting the context to the uncommon outcome. LLM-enhanced crowd-written explanations achieve the highest quality by combining the strengths of both.

The authors experiment with two online imitation learning methods, EaO (Expert as Oracle) and SED (Static Expert Demonstrations), to train open and accessible language models on this task. These methods show consistent improvements over the standard supervised fine-tuning approach, reducing the loss rate against a strong LLM baseline on both commonsense and uncommonsense abductive reasoning.

統計資料

20,947 context-outcome pairs in total, with 3,539 from un-RocStories and 17,408 from un-SocialIQA
41,711 crowd-sourced explanations, 41,375 LLM-enhanced crowd-written explanations, and 58,881 LLM-generated explanations

引述

"Language technologies that accurately model the dynamics of events must perform commonsense reasoning. Existing work evaluating commonsense reasoning focuses on making inferences about common, everyday situations. To instead investigate the ability to model unusual, unexpected, and unlikely situations, we explore the task of uncommonsense abductive reasoning."
"Given a piece of context with an unexpected outcome, this task requires reasoning abductively to generate a natural language explanation that makes the unexpected outcome more likely in the context."

從以下內容提煉的關鍵洞見

UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations

by Wenting Zhao... 於 arxiv.org 05-02-2024

https://arxiv.org/pdf/2311.08469.pdf

深入探究

What are some potential real-world applications of uncommonsense abductive reasoning beyond language understanding?

Uncommonsense abductive reasoning can have various real-world applications beyond language understanding. One potential application is in the field of artificial intelligence and robotics, where machines need to make decisions in uncommon or unexpected situations. For example, autonomous vehicles may encounter unusual road conditions or scenarios that require quick decision-making based on abductive reasoning. Similarly, in healthcare, medical AI systems could use uncommonsense abductive reasoning to diagnose rare or atypical medical conditions that do not fit typical patterns.
Another application could be in the field of cybersecurity, where AI systems need to detect and respond to novel cyber threats or attacks that deviate from known patterns. By employing uncommonsense abductive reasoning, these systems can better anticipate and mitigate emerging threats.
Additionally, uncommonsense abductive reasoning can be valuable in the field of scientific research, where researchers often encounter unexpected results or anomalies that require creative reasoning to explain. By leveraging abductive reasoning, scientists can generate hypotheses and explanations for unconventional findings, leading to new discoveries and insights.

How could the UNCOMMONSENSE dataset be extended to cover a broader range of uncommon scenarios, such as those involving complex social interactions or scientific reasoning?

To extend the UNCOMMONSENSE dataset to cover a broader range of uncommon scenarios, such as complex social interactions or scientific reasoning, several approaches can be taken:

Diversifying Contexts: Include a wider variety of contexts that involve intricate social dynamics, ethical dilemmas, or scientific phenomena. This can be achieved by sourcing scenarios from diverse sources such as literature, case studies, or real-world events.

Expert Contributions: Collaborate with domain experts in social sciences, psychology, or scientific fields to curate scenarios that involve complex interactions or phenomena. Experts can provide insights into uncommon situations that require abductive reasoning.

Crowdsourcing: Expand the crowdsourcing efforts to collect explanations for a broader range of scenarios. Encourage contributors to provide explanations for complex social or scientific scenarios, fostering creativity and diversity in the dataset.

Fine-tuning Language Models: Fine-tune language models on additional data sources related to social interactions or scientific reasoning to enhance their ability to generate explanations for a wider range of uncommon scenarios.

By incorporating these strategies, the UNCOMMONSENSE dataset can be enriched with a more diverse set of scenarios, enabling language models to engage in uncommonsense abductive reasoning across various domains.

How might the performance of language models on this task relate to their ability to engage in open-ended creative reasoning about novel situations?

The performance of language models on uncommonsense abductive reasoning tasks can provide insights into their ability to engage in open-ended creative reasoning about novel situations. Here are some ways in which performance on this task may relate to their creative reasoning abilities:

Flexibility in Generating Explanations: Language models that excel at uncommonsense abductive reasoning demonstrate a higher degree of flexibility in generating explanations for unexpected outcomes. This flexibility indicates their capacity to think creatively and consider diverse possibilities in novel situations.

Connection of Context and Outcome: Models that perform well on this task can effectively bridge the gap between a given context and an uncommon outcome through logical and coherent explanations. This ability reflects their capacity for creative problem-solving and reasoning in unfamiliar scenarios.

Incorporation of Contextual Cues: Language models that can leverage contextual cues and background knowledge to generate plausible explanations for uncommon outcomes showcase their aptitude for creative thinking and adaptive reasoning in novel contexts.

Exploration of Alternative Scenarios: Models that can explore and consider multiple alternative scenarios in uncommonsense abductive reasoning demonstrate a higher level of creativity in generating explanations for unexpected events. This ability to explore diverse possibilities reflects their capacity for open-ended creative reasoning.

Overall, the performance of language models on uncommonsense abductive reasoning tasks can serve as a proxy for their ability to engage in open-ended creative reasoning about novel situations, highlighting their capacity for adaptive, flexible, and imaginative thinking.