Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)
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| Challenge: | Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge. |
| Approach: | They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities. |
| Outcome: | The proposed model exhibits performance levels comparable to current SOTA LLMs across a spectrum of tasks. |
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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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Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)
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| Challenge: | Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers. |
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Adaptation of Large Language Models (2025.naacl-tutorial)
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| Challenge: | a tutorial on adaptation of large language models addresses the growing demand for models that go beyond static capabilities. |
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Trained on 100 million words and still in shape: BERT meets British National Corpus (2023.findings-eacl)
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| Challenge: | masked language models are trained on ever larger corpora, but pre-training on a modestly-sized but representative, well-balanced, and publicly available corpus can reach better performance than the original BERT model. |
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Scaling Laws for BERT in Low-Resource Settings (2023.findings-acl)
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| Challenge: | Large language models require huge training corpora, which is unobtainable for most NLP practitioners. |
| Approach: | They propose power-law formulas that relate model size, corpora size and computation power to find the optimal settings in advance given a fixed budget. |
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Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (2024.emnlp-main)
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| Challenge: | Pre-trained language models have limited generalization capabilities and performance challenges. |
| Approach: | They evaluate 15 different backbone LLMs and non-LLMs to evaluate their performance . larger models and extensive pre-training consistently enhance in-domain accuracy and data efficiency . |
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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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Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs? (2025.findings-emnlp)
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| Challenge: | Rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. |
| Approach: | They compare BERT-like models fine-tuning, LLM internal state utilization, and LLM zero-shot inference across six datasets. |
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Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
| Approach: | This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks . |
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Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
| Approach: | They propose a model that integrates symbolic data into LLM training without loss of generality ability. |
| Outcome: | The proposed model performs better on symbol- and NL-centric tasks. |