Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models? (2024.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that stories outperform rules as the expression for retrieving commonsense from LLMs, exhibiting higher generation confidence and commonsensense accuracy. |
| Approach: | They investigate the commonsense ability of large language models expressed through stories and rules to retrieve commonsensing knowledge from LLMs. |
| Outcome: | The stories outperform rules as commonsense expressions on 28 commonsensense QA datasets, exhibiting higher generation confidence and commonsence accuracy. |
Similar Papers
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. |
| Approach: | They conduct a comprehensive examination of the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks. |
| Outcome: | The language used to query the LLMs can impact their performance on cultural-related tasks. |
On the Consistency of Commonsense in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluations of commonsense for large language models focus on downstream knowledge tasks, failing to probe whether LLMs truly understand and utilize knowledge or merely memorize it. |
| Approach: | They propose to automatically construct a large benchmark named CoCo which measures LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
| Outcome: | The proposed benchmark systematically assesses LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
Copied to clipboard
Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
Copied to clipboard
| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
| Approach: | They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |
A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022.emnlp-main)
Copied to clipboard
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, Aida Nematzadeh
| Challenge: | Recent large language models (LMs) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. |
| Approach: | They conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained language models to better understand their ability to capture commonsensical knowledge. |
| Outcome: | The proposed model can exploit surface cues and annotation artefacts without task-specific supervision and is insufficient to achieve human-level commonsense performance. |
Do Children Texts Hold The Key To Commonsense Knowledge? (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to compiling commonsense knowledge (CSK) struggle with reporting bias, i.e., frequency in text sources is not a good proxy for relevance or truth. |
| Approach: | They propose that children's texts make fewer assumptions on the reader's knowledge and therefore spell out commonsense more explicitly. |
| Outcome: | The proposed approach can be leveraged in language-model-based commonsense knowledge extraction settings, where task-unspecific fine-tuning on small amounts of children texts yields significant improvements. |
Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)
Copied to clipboard
Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng
| Challenge: | a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories . |
| Approach: | They propose a computational framework to analyze narratives through three discourse-level aspects. |
| Outcome: | The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding . |
Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
| Approach: | They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare . |
| Outcome: | The proposed approach overestimates the rare at the expense of the rare, while minimizing reporting bias. |
Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
Copied to clipboard
Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
| Approach: | They analyze the effects of model size and methods of injecting knowledge into TellMeWhy datasets to determine what aspects of commonsense knowledge are available in large language models. |
| Outcome: | The largest models yield substantial improvements over base models, but the amount of improvement decreases with larger model size. |