Papers with FI
Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models (2026.eacl-long)
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| Challenge: | Large Language Models and Vision Language Model (LLMs) have impressive performance across a wide range of tasks, yet remain vulnerable to external and internal perturbations. |
| Approach: | They propose a stability measure called FI, First order local Influence, which quantifies the sensitivity of individual parameters and input dimensions. |
| Outcome: | The proposed stability measure measures the sensitivity of parameters and input dimensions across LLMs and VLMs. |
Combining ELECTRA and Adaptive Graph Encoding for Frame Identification (2022.lrec-1)
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| Challenge: | Existing studies focus on FI tasks, but none have been done on the computational side. |
| Approach: | They propose a new system for Frame Identification based on pre-trained text encoders trained discriminatively and graphs embedding. |
| Outcome: | The proposed system produces state-of-the-art performance over two benchmarks and all possible splits and cleaning procedures used in the literature. |
Towards preserving word order importance through Forced Invalidation (2023.eacl-main)
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| Challenge: | Recent studies show pre-trained language models are insensitive to word order . performance on NLU tasks remains unchanged even after permuting the word . |
| Approach: | They propose a simple approach called Forced Invalidation to force the model to identify permuted sequences as invalid samples. |
| Outcome: | The proposed approach significantly improves the sensitivity of the models to word order on English NLU and QA tasks over BERT-based and attention-based models over word embeddings. |
A Knowledge-Guided Framework for Frame Identification (2021.acl-long)
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| Challenge: | Existing frameworks for frame identification are limited to only a few types of frame knowledge. |
| Approach: | They propose a Knowledge-Guided Frame Identification framework that integrates frame knowledge to learn better frame representation. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two benchmark datasets. |