Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Das, Preslav Nakov
| Challenge: | Large language models (LLMs) are factually incorrect, which limits their applicability in real-world scenarios. |
| Approach: | They analyze existing work to identify major challenges and their associated causes . they propose to evaluate LLMs using a variety of measures to mitigate factual errors . |
| Outcome: | The proposed methods are based on a variety of datasets and proposed strategies to mitigate factual errors. |
Similar Papers
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
Copied to clipboard
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
| 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. |
Knowledge Boundary of Large Language Models: A Survey (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) store vast amount of knowledge in their parameters, but they still have limitations in the memorization and utilization of certain knowledge. |
| Approach: | They propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. |
| Outcome: | The proposed definition of the LLM knowledge boundary and taxonomy categorizes knowledge into four distinct types . aims to offer a comprehensive overview, facilitate access to key issues, and inspire further advancements in LLM research. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.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 Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses. |
| Approach: | They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks . |
| Outcome: | The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem . |
A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
Copied to clipboard
| 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. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
Copied to clipboard
| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (2023.emnlp-main)
Copied to clipboard
| 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. |
When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation (2026.eacl-short)
Copied to clipboard
| Challenge: | Existing studies on LLM factuality evaluation have not investigated the reliability of static evaluation benchmarks. |
| Approach: | They examine five popular factuality benchmarks and eight LLMs released over different years to assess their reliability. |
| Outcome: | The proposed method compared five popular factuality benchmarks and eight LLMs released over different years. |
Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used for accessing information on the web. |
| Approach: | They conduct experiments with 80 crowdworkers to compare LLMs with search engines . they ask LLM to provide contrastive information to reduce over-reliance on LLM . |
| Outcome: | The results show that LLMs can outperform search engines but not LLM explanations . the study shows that LMS explanations are not reliable replacements for reading retrieved passages compared to search engines alone. |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |