| Challenge: | OOMB is a novel benchmark designed to assess the ability of large language models (LLMs) to extract and analyze opinions from diverse and complex online environments. |
| Approach: | They propose an online opinion mining benchmark to assess the ability of large language models to extract and analyze opinions from diverse online environments. |
| Outcome: | The proposed benchmark assesses the ability of large language models to mine opinions effectively from diverse and complex online environments. |
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Evaluating the Performance of Large Language Models via Debates (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are evolving and impacting various fields . current methods for evaluation are based on fixed, domain-specific questions or rely on human input, making them unscalable. |
| Approach: | They propose a benchmarking framework based on debates between LLMs, judged by another LLM. |
| Outcome: | The proposed framework achieves rankings that align closely with popular rankings based on human input eliminating the need for costly crowdsourcing. |
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
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Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| 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. |
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Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
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From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models (2025.naacl-srw)
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| Challenge: | Using a synthetic sports feedback dataset, we evaluate open-weight LLMs’ ability to extract aspect-polarity pairs. |
| Approach: | They propose a metric to facilitate the evaluation of aspect extraction with generative models. |
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Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
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Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. |
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Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
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| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
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Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews (2025.findings-acl)
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| Challenge: | Existing large language models (LLMs) are difficult to evaluate and often lack the ability to capture user opinions. |
| Approach: | They propose an LLM-powered interviewer that conducts in-the-moment user experience interviews right after users interact with LLMs and automatically gathers insights about user opinions from massive interview logs. |
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Can Language Models Recognize Convincing Arguments? (2024.findings-emnlp)
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| Challenge: | Existing studies have found that large language models can generate persuasive content without engaging in human experimentation. |
| Approach: | They extend a dataset with debates, votes, and user traits to measure LLMs' ability to distinguish between strong and weak arguments, predict stances based on beliefs and demographic characteristics, and determine appeal of argument to individual based upon their traits. |
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