| Challenge: | Large language models produce high quality information at unprecedented rates . content produced by these models is propagated throughout forums that influence other models and human users . |
| Approach: | They propose a method for representing the perspective of individual models within a collection of LLMs. |
| Outcome: | The proposed method represents the perspective of individual models within a collection of LLMs in various simulated settings. |
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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han
| Challenge: | Recent studies have discovered notable disparities in their performance across different languages. |
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| Outcome: | The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios. |
Data and Model Centric Approaches for Expansion of Large Language Models to New languages (2025.emnlp-tutorials)
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| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
| Approach: | This tutorial examines approaches to expand the language coverage of LLMs . they look at tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc. |
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From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models (2026.findings-acl)
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| Challenge: | Existing definitions of streaming LLMs are fragmented and lack a systematic taxonomy . large language models are pre-trained on static and full-context corpora . |
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How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
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Continual Learning of Large Language Models (2025.emnlp-tutorials)
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| Challenge: | This tutorial explores the challenges of continual learning in large language models . participants will learn strategies to mitigate forgetting and manage data and evaluation pipelines . |
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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
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Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025.coling-main)
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| Challenge: | Popularity of Large Language Models (LLMs) has seen a skyrocketing increase in recent years. |
| Approach: | They present a systematic review of the language adaptation process for Large Language Models including vocabulary expansion, continued pre-training, and instruction fine-tuning. |
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When Large Language Models Meet Speech: A Survey on Integration Approaches (2025.findings-acl)
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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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From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2025.acl-long)
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Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos, Lucas Weber, Alberto Testoni, Marzieh Fadaee, Sandro Pezzelle, Marco Del Tredici
| Challenge: | Large Language Models excel at solving individual problems in isolation, but are they able to effectively collaborate over long-term interactions? |
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