What Matters in Memorizing and Recalling Facts? Multifaceted Benchmarks for Knowledge Probing in Language Models (2024.findings-emnlp)
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| Challenge: | Language models often exhibit factual hallucination issue, exhibiting factual factual knowledge-grounded sentences. |
| Approach: | They introduce a knowledge probing benchmark to evaluate the knowledge recall ability of pre-trained language models from diverse perspectives. |
| Outcome: | The proposed benchmark evaluates the knowledge recall ability of encoder- and decoder-based pre-trained language models from diverse perspectives. |
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| Challenge: | Pre-trained language models are trained on vast unlabeled data, rich in world knowledge. |
| Approach: | They propose a categorization scheme for factual probing methods based on how inputs, outputs and probed PLMs are adapted . they synthesize insights about knowledge retention and prompt optimization in PLM models and analyze obstacles to adopting them as knowledge bases . |
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| Challenge: | Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks. |
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X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models (2020.emnlp-main)
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| Challenge: | Language models (LMs) capture factual knowledge by filling in the blanks of cloze-style prompts. |
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| Challenge: | Existing studies show that Pretrained Language Models can store factual knowledge, but facts stored in PLMs are not always correct. |
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FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation (2025.acl-long)
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| Challenge: | Language models (LMs) generate false or unverifiable content, often known as hallucination, despite ongoing efforts to enhance their factuality. |
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| Challenge: | Factual knowledge of LLMs is typically evaluated using accuracy, yet this metric does not capture the vulnerability of LRMs to hallucination-inducing factors like prompt and context variability. |
| Approach: | They propose a metric designed to measure LLMs’ factual reliability by comparing the distance between the probability distributions of a valid output and its counterparts produced by the same LLM probing the same fact using different styles of prompts and contexts. |
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Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
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| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
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How Do Multilingual Language Models Remember Facts? (2025.findings-acl)
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| Challenge: | Prior research has focused on English monolingual models, but how these mechanisms generalize to non-English languages remains unexplored. |
| Approach: | They analyze three multilingual LLMs to find out how they can generalize recall mechanisms . they find that subject enrichment is language-independent, object extraction is language dependent . |
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Tracing Multilingual Factual Knowledge Acquisition in Pretraining (2025.findings-emnlp)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Felicia Körner, Ercong Nie, Barbara Plank, François Yvon, Hinrich Schuetze
| Challenge: | Large Language Models are capable of recalling multilingual factual knowledge, but most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency unexplored. |
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Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021.naacl-main)
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| Challenge: | Existing methods for factual probing can interpret the model’s prediction accuracy as a lower bound on the amount of factual information it encodes. |
| Approach: | They propose a method which directly optimizes in continuous embedding space and can predict an additional 6.4% of facts in the LAMA benchmark. |
| Outcome: | The proposed method outperforms the best previous prompt method by 6.4% on the LAMA benchmark. |