If Eleanor Rigby Had Met ChatGPT: A Study on Loneliness in a Post-LLM World (2025.acl-long)
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| Challenge: | Loneliness is a global health concern and is prevalent worldwide . |
| Approach: | They analysed user interactions with ChatGPT outside of its marketed use as a task-oriented assistant and found that LLMs are more prevalent and riskier than LLM-based services . |
| Outcome: | The proposed models modify the LLMs to respond to loneliness and provide better engagement in conversations. |
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Shir Lissak, Nitay Calderon, Geva Shenkman, Yaakov Ophir, Eyal Fruchter, Anat Brunstein Klomek, Roi Reichart
| Challenge: | Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation. |
| Approach: | They propose a scale that is inspired by psychological standards and expert input to evaluate LLM's interactions with queer-related content. |
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Toxicity in chatgpt: Analyzing persona-assigned language models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing community. |
| Approach: | They evaluate toxicity in over half a million generations of ChatGPT by assigning it a persona . they find that outputs engage in incorrect stereotypes, harmful dialogue, hurtful opinions . |
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A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets (2023.findings-acl)
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Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, Jimmy Huang
| Challenge: | Currently, the evaluation of large language models (LLMs) such as ChatGPT in academic datasets is difficult due to the difficulty of evaluating the generative outputs produced by this model against the ground truth. |
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ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
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| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
| Approach: | They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications. |
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Exploring Inherent Biases in LLMs within Korean Social Context: A Comparative Analysis of ChatGPT and GPT-4 (2024.naacl-srw)
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| Challenge: | Large Language Models (LLMs) have been criticized for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes. |
| Approach: | They devised a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences. |
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First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models (2024.naacl-long)
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| Challenge: | a new system trained on well over a trillion words smashes the state of the art by a margin previously thought impossible. |
| Approach: | They argue that disparities in scale are transient and researchers can work to reduce them . they argue that data, rather than hardware, is still a bottleneck for many applications . |
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Multi-step Jailbreaking Privacy Attacks on ChatGPT (2023.findings-emnlp)
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| Challenge: | With the rapid evolution of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. |
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Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements (2023.findings-emnlp)
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| 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. |
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Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Existing evaluation protocols for large language models (LLMs) are inadequate for conversational recommender systems. |
| Approach: | They propose an evaluation approach based on LLMs that harnesses LLM-based user simulators to evaluate ChatGPT's performance. |
| Outcome: | The proposed evaluation approach can simulate various system-user interaction scenarios. |
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness (2023.emnlp-main)
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| Challenge: | generative large language models (LLMs) are replacing human workers for some tasks . crowdsourcing has several downsides: 1) the workforce is costly, 2) output quality is difficult to achieve, and 3) there are overheads related to the design and organization of the process. |
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| Outcome: | The proposed models are more diverse and robust than the existing models. |