Perspective Taking through Generating Responses to Conflict Situations (2024.findings-acl)
Copied to clipboard
| Challenge: | Language models struggle to understand and explain the beliefs of others, despite improving performance on a wide variety of tasks. |
| Approach: | They propose to modify the social-chem-101 corpus to allow for perspective-taking, the process of conceptualizing the point of view of another person. |
| Outcome: | The proposed models outperform the recent models conditioned on self-disclosures with high similarity to the conflict situation. |
Similar Papers
Proposal: From One-Fit-All to Perspective Aware Modeling (2025.acl-srw)
Copied to clipboard
| Challenge: | Variation in human annotation and human perspectives has drawn increasing attention in natural language processing research. |
| Approach: | They propose to use annotation formats that better capture granularity and uncertainty of individual judgments and annotation modeling that leverages socio-demographic features to better represent and predict underrepresented or minority perspectives. |
| Outcome: | The proposed tasks aim to advance natural language processing research towards more faithfully reflecting the diversity of human interpretation, enhancing both inclusiveness and fairness in language technologies. |
Towards Multi-Perspective NLP Systems: A Thesis Proposal (2025.acl-srw)
Copied to clipboard
| Challenge: | Existing approaches to resolving disagreements ignore individual opinions and can result in the marginalization of minority perspectives. |
| Approach: | They propose to preserve individual labels in human-annotated datasets for subjective tasks and propose solutions for developing Perspective-Aware by design systems. |
| Outcome: | The proposed framework will be used to develop more responsible and inclusive models. |
Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Empathetic dialogue is an essential part of building harmonious social relationships and contributes to the development of a helpful AI. |
| Approach: | They propose three methods to improve the performance of large language models (LLMs) they propose semantically similar in-context learning, two-stage interactive generation and combination with the knowledge base. |
| Outcome: | The proposed methods achieve state-of-the-art in automatic and human evaluations and the possibility of GPT-4 simulating human evaluators. |
Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing prompting methods that require white-box access to the model or substantial training fail to simultaneously lessen toxicity and bias. |
| Approach: | They propose a strategy that encourages LLMs to integrate diverse human perspectives and self-regulate their responses by incorporating diverse human viewpoints. |
| Outcome: | The proposed approach can significantly diminish toxicity (up to 89%) and bias (up 73%) in LLMs’ responses. |
Perspective Transition of Large Language Models for Solving Subjective Tasks (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing . performance of LLMs on subjective tasks is limited, authors say . |
| Approach: | They propose a method that allows LLMs to select between direct, role, and third-person perspectives for best way to solve corresponding subjective problem. |
| Outcome: | The proposed method outperforms widely used single fixed perspective based methods on 12 subjective tasks. |
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims (N19-1)
Copied to clipboard
| Challenge: | a number of fact checking techniques are used to identify and eliminate biases in text data. |
| Approach: | They propose to use search engines to expand and diversify a dataset of claims, perspectives and evidence to address a selection bias. |
| Outcome: | The proposed approach outperforms existing methods in a language understanding task. |
Think Twice: Perspective-Taking Improves Large Language Models’ Theory-of-Mind Capabilities (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances to LLMs’ reasoning capabilities from simple yet effective prompting techniques such as Chain-of-Thought have seen limited applicability to ToM. |
| Approach: | They propose a two-stage prompting framework inspired by Simulation Theory's notion of perspective-taking to elicit Theory-of-Mind capabilities in Large Language Models. |
| Outcome: | The proposed framework shows that it is much more effective than existing prompts. |
I’m sure you’re a real scholar yourself: Exploring Ironic Content Generation by Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Moreover, irony is highly subjective and can depend on various factors, such as social, cultural, or generational aspects. |
| Approach: | They propose to fine-tune two large language models to generate ironic and non-ironic content and analyze their outputs from a linguistic perspective. |
| Outcome: | The proposed models generate ironic and non-ironic responses to a given social media post and analyze their outputs from a linguistic perspective. |
Topicalization in Language Models: A Case Study on Japanese (2022.coling-1)
Copied to clipboard
| Challenge: | a recent study has shown that neural language models can capture discourse-level preferences in text generation . a particular aspect of discourse is the topic-comment structure . |
| Approach: | They analyze whether neural language models can capture discourse-level preferences in text generation . they use Japanese language and crowdsourced human topicalization judgment data . |
| Outcome: | The proposed model can capture human-like generalizations in discourse-level linguistic aspects. |
LLM generated responses to mitigate the impact of hate speech (2024.findings-emnlp)
Copied to clipboard
Jakub Podolak, Szymon Łukasik, Paweł Balawender, Jan Ossowski, Jan Piotrowski, Katarzyna Bakowicz, Piotr Sankowski
| Challenge: | a study aims to determine the effectiveness of large language models to counteract hate speech . it is the first real-life A/B test evaluating the effectiveness . |
| Approach: | They conduct the first real-life A/B test assessing the effectiveness of LLM-generated counter-speech. |
| Outcome: | The proposed system reduces user engagement by over 20%, the study shows . the proposed metric is based on a simple metric and is scalable to other platforms . |