| Challenge: | Social commonsense contains many human biases due to social and cultural influence. |
| Approach: | They aim to identify cultural biases in data that strongly influence model decisions . they use social commonsense knowledge to augment large-scale language models . |
| Outcome: | The proposed method shows that social commonsense knowledge can explain model behavior on two social tasks. |
Similar Papers
Debiasing Event Understanding for Visual Commonsense Tasks (2022.findings-acl)
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| Challenge: | a recent study shows that object-based event understanding is purely likelihood-based, leading to incorrect event prediction. |
| Approach: | They propose to mitigate object-based event understanding by optimizing aggregation with association-based prediction. |
| Outcome: | The proposed approach improves visual commonsense reasoning tasks by combining do-calculus with association-based prediction. |
A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
| Outcome: | The proposed method can solve 104k commonsense inference problems in a Japanese corpus with high accuracy, but low bias. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| 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). |
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (P18-1)
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| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
| Approach: | They construct a crowdsourced corpus of 25,000 event phrases and use them to construct 'commonsense inference' they demonstrate that neural encoder-decoder models can compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. |
| Outcome: | The proposed task can be used to uncover implicit gender inequality in movie scripts. |
Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models have enabled automatic generation of chain-of-thought reasoning . however, when reasoning steps reflect social stereotypes, they can reinforce harmful associations and lead to misleading conclusions. |
| Approach: | They propose a method that detects how model predictions change across incremental reasoning steps. |
| Outcome: | The proposed method outperforms a stereotype-free baseline and improves accuracy. |
Bias Mitigation or Cultural Commonsense? Evaluating LLMs with a Japanese Dataset (2025.emnlp-main)
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| Challenge: | Recent studies have demonstrated that large language models exhibit social biases . however, debiasing methods may degrade the capabilities of LLMs if they are not properly evaluated . |
| Approach: | They propose a Japanese benchmark to evaluate social biases and cultural commonsense in large language models in a unified format. |
| Outcome: | The proposed method degrades the performance of the LLMs on the cultural commonsense task by 75%. |
SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models (2023.eacl-main)
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| Challenge: | Existing diagnostic tests for detecting social biases in NLP models only detect stereotypic associations pre-specified by the designer. |
| Approach: | They propose an approach for automatic social bias discovery in social commonsense question-answering by substituting names associated with different demographic groups and generating many distractor answers from a masked language model. |
| Outcome: | The proposed approach uncovers model’s stereotypic associations between demographic groups and an open set of words. |
Social Bias Frames: Reasoning about Social and Power Implications of Language (2020.acl-main)
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| Challenge: | Language has enormous power to project social biases and reinforce stereotypes on people. |
| Approach: | They propose a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and power differentials onto others. |
| Outcome: | The proposed model can model the pragmatic frames in which people project social biases and power differentials onto others. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
| Outcome: | The proposed model-generated stories are based on a commonsense reasoning engine and are able to uncover gender biases in the protagonist's motivations, attributes, mental states, and implications on others. |