How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies have focused on enhancing the factualness of large language models using context knowledge. |
| Approach: | They propose to use ChatGPT to construct probing datasets that provide diverse and coherent evidence corresponding to various facts. |
| Outcome: | The proposed model can encode knowledge across different layers, and it is compared with existing models. |
Similar Papers
Do Large Language Models Know How Much They Know? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models are highly capable systems, but their capabilities and limitations are unclear. |
| Approach: | They develop a benchmark that challenges LLMs to recall all information they possess on specific topics. |
| Outcome: | The proposed model can recall excessive, insufficient, or the precise amount of information they possess on a given topic, indicating their awareness of how much they know about the given topic. |
Reassessing Semantic Knowledge Encoded in Large Language Models through the Word-in-Context Task (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have propelled significant progress, extending their application across various domains including dialogue systems, text generation, translation systems, and beyond. |
| Approach: | They propose to use the Word-in-Context (WiC) task to reassess the semantic knowledge encoded in large language models (LLMs) they prompt LLMs to generate natural language descriptions that contrast the meanings of the target word in two contextual sentences given in the WiC dataset. |
| Outcome: | The proposed model significantly improves the classification accuracy of the two models. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
Copied to clipboard
Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
Language Models Struggle to Use Representations Learned In-Context (2026.acl-long)
Copied to clipboard
| Challenge: | a recent study shows that large language models are capable of inducing rich representations of data that are seen in-context . a novel task, adaptive world modeling, shows that even the most performant LLMs cannot reliably leverage novel semantics defined in-constitut. |
| Approach: | They propose to use in-context representations to induce rich representations of data . they also propose to probe models using a novel task to enable flexible deployment . |
| Outcome: | The proposed model can use in-context representations to complete simple downstream tasks. |
Estimating Knowledge in Large Language Models Without Generating a Single Token (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to evaluate knowledge in large language models require querying and evaluating the model's generated responses. |
| Approach: | They ask whether it is possible to estimate how knowledgeable a model is about a subject entity only from its internal computation. |
| Outcome: | The proposed model performs well with QA accuracy and FActScore . it can be leveraged to guide decisions on how to apply further training or augment queries with retrieval. |
Can Large Language Models Understand Context? (2024.findings-eacl)
Copied to clipboard
Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng
| Challenge: | Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features. |
| Approach: | They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation. |
| Outcome: | The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models. |
Document-Level Machine Translation with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks. |
| Approach: | They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4. |
| Outcome: | The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation. |
Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semantics (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing research suggests that contextual representations of large language models exhibit subpar performance in downstream tasks, struggling to fully capture the semantic nuances of words. |
| Approach: | They investigate the bottom-up evolution of lexical semantics for a popular LLM . they probing its hidden states at the end of each layer using a contextualized word identification task . |
| Outcome: | The proposed model is able to encode lexical semantics in lower layers while achieving weaker induction in higher layers. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
Copied to clipboard
Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
| Approach: | They examine the probing paradigm through a set of controlled synthetic tasks and show that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself. |
| Outcome: | The proposed model can encode linguistic properties above chance-level even when distributed in the data as random noise, reversing the interpretation of absolute claims on probing tasks. |