Papers with probing approach
Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers (2021.naacl-main)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) has attracted wide attention in recent years. |
| Approach: | They propose a probing-based approach to measure word translation accuracy using transformer layers. |
| Outcome: | The proposed model outperforms previous probing-based translation models. |
Sort by Structure: Language Model Ranking as Dependency Probing (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing algorithms for pre-trained language models lack performance indicators for linguistic tasks such as structured prediction. |
| Approach: | They propose to measure the degree to which labeled trees are recoverable from an LM’s contextualized embeddings by probing to rank LMs for parsing dependencies in a given language. |
| Outcome: | The proposed approach predicts the best LM choice 79% of the time using less compute than training a full parser. |
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to evaluate LMs rely on objective function and are therefore limited to masked or causal LM types. |
| Approach: | They propose an approach that uses an LM’s inherent ability to estimate the log-likelihood of any given textual statement. |
| Outcome: | The proposed framework can probe for knowledge across different LM types. |
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models (2023.emnlp-main)
Copied to clipboard
Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan
| Challenge: | Recent work has shown that language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities. |
| Approach: | They propose a mechanistic interpretation of language models for multi-step reasoning tasks by introducing a new probing approach that recovers the reasoning tree from the model’s attention patterns. |
| Outcome: | The proposed model implicitly embeds a reasoning tree resembling the correct reasoning process within it, and detects the information from the model’s attention patterns for most examples. |