Challenge: Large language models (LLMs) have been widely used in various industries due to their unprecedented scale and impressive capabilities derived from the massive training dataset.
Approach: They propose a framework that can estimate the influence of training data by caching and retrieval.
Outcome: The proposed framework can estimate the influence of training data within minutes, achieving over a speedup of 6,326x.

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Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models (2025.naacl-long)

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Challenge: Lack of transparency in training data is limiting external oversight and inspection of LLMs for issues such as copyright infringement and data contamination.
Approach: They propose a method to identify training data known to proprietary LLMs without requiring access to model weights or token probabilities by using information-guided probes.
Outcome: The proposed method can identify training data known to proprietary LLMs without access to model weights or token probabilities.
A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

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Challenge: Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach: They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome: The proposed model can be used to understand and generate human natural languages.
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)

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Challenge: achieving data-efficient post-training of Large Language Models is a key research question.
Approach: They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective.
Outcome: The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems.
Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction (2026.findings-acl)

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Challenge: Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long context inputs, but this comes at the cost of increased computational resources and latency.
Approach: They propose an algorithm that uses early LLM layers as filters to select and compress input tokens, reducing the context length for subsequent processing.
Outcome: The proposed method outperforms existing techniques on the Needle in a Haystack task while demonstrating comparable performance on the LongBench challenge.
TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use (2025.findings-emnlp)

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Challenge: a new approach to training large language models (LLMs) overlooks task-specific characteristics in tool use, leading to performance bottlenecks.
Approach: They propose a task-feature-based framework that mitigates the effects of suboptimal training data . they use a dataset to train large-scale LLMs and a reward mechanism tailored to error categories .
Outcome: The proposed framework matches or surpasses open- and closed-source LLMs in tool-use performance using only 1,217 training data points.
The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)

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Challenge: acquiring and curating high-quality training data remains a significant bottleneck . acquiring such high-quality data is a key challenge for researchers and practitioners .
Approach: This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs.
Outcome: The tutorial covers methods for curating the most valuable information from vast, noisy datasets and the synthetic data revolution.
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.
Outcome: This tutorial examines approaches to expand the language coverage of LLMs . it provides an efficient and viable path to bring LLM technologies to low-resource languages .
Aligning Large Language Models via Fine-grained Supervision (2024.acl-short)

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Challenge: Pre-trained large-scale language models often generate biased or toxic text, misaligning with human intentions.
Approach: They propose to use human feedback to improve LLM alignment by fine-grained token supervision . they ask annotators to edit less preferred responses to make them more favorable .
Outcome: The proposed method improves LLM alignment by up to 5.1% in terms of win rate compared with the traditional model.
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models (2024.findings-emnlp)

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Challenge: Large Language Models (LLMs) exhibit surprising abilities across a variety of language tasks.
Approach: They propose an algorithm which selects a coreset by analyzing correlation between training and evaluation samples with a trained model.
Outcome: The proposed algorithm can achieve similar performance with just 50% of the training data while preserving the accuracy of the existing model.
LongAttn: Selecting Long-context Training Data via Token-level Attention (2025.findings-acl)

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Challenge: Existing methods to select long-context data often rely on sentence-level analysis, which can be greatly optimized in both performance and efficiency.
Approach: They propose a token-level framework which quantifies long-range dependencies for LLMs by calculating token-based dependency strength and distribution uniformity of token scores.
Outcome: The proposed framework quantifies long-range dependencies, enabling more accurate and efficient data selection.

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