Challenge: Incorporating human feedback into Large Language Models is a welcome development, but it introduces new biases and challenges.
Approach: They propose to survey 95 articles that use human feedback to steer, guide or tailor the behaviours of large language models.
Outcome: The proposed approaches are based on 95 articles primarily from the ACL and arXiv repositories and highlight five unresolved conceptual and practical challenges.

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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Challenge: Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.
Approach: They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge.
Outcome: The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses.
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains.
Approach: They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks .
Outcome: The proposed evaluations are reproducible, reliable, and robust.
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)

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Challenge: General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself.
Approach: This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods.
Outcome: The tutorial assumes little familiarity with metrics, datasets, prompts and benchmarks . it will compare traditional methods to newly developed methods .
A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)

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Challenge: Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations.
Approach: They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned.
Outcome: The proposed methods can be used to assess the reliability of models and to calibrate them across tasks.
Current Advances in LLM Reasoning (2026.acl-tutorials)

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Challenge: This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial.
Approach: This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO.
Outcome: This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning.
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models (2024.findings-emnlp)

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Challenge: Recent advances in Large Language Models have sparked interest in validating human-like cognitive-behavioral traits.
Approach: They examine whether LLM outputs reflect human-like cognitive-behavioral traits . they find that measuring AOVs embedded within LLMs remains opaque .
Outcome: The proposed model can be used to evaluate human-like cognitive-behavioral traits . the proposed model could be used in writing assistants and other applications .
Awes, Laws, and Flaws From Today’s LLM Research (2025.findings-acl)

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Challenge: Large language models (LLMs) are a powerful technology that can follow instructions and output coherent, persuasive text.
Approach: They examine the scientific methodology behind large language model (LLM) research and cross-validate it with arguments at the centre of controversy.
Outcome: The authors cross-validate 2,000 research works released between 2020 and 2024 based on criteria typical of what is considered good research and find that conference checklists are effective at curtailing some of these issues, but balancing velocity and rigour in research cannot solely rely on these.
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

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Challenge: Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach: They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome: The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (2023.emnlp-main)

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Challenge: Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text.
Approach: They argue that LLMs only parrot statistical patterns in training data and that language learning in LLM cannot inform human language learning.
Outcome: The proposed model can generate grammatically correct, fluent text without requiring human intervention.
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 .
Approach: This tutorial offers a comprehensive exploration of continual learning in the context of large language models.
Outcome: This tutorial explores the challenges of continual learning in large language models . participants will learn how to manage data and evaluation pipelines and adapt responsibly .

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