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Found 6 papers, 5 papers with code
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e. g., GPUs) or require the collection of specific training data.
FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts
Ensuring the safety of artificial intelligence-generated content (AIGC) is a longstanding topic in the artificial intelligence (AI) community, and the safety concerns associated with Large Language Models (LLMs) have been widely investigated.
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition
In particular, audio and visual front-ends are trained on large-scale unimodal datasets, then we integrate components of both front-ends into a larger multimodal framework which learns to recognize parallel audio-visual data into characters through a combination of CTC and seq2seq decoding.
Ranked #2 on
Automatic Speech Recognition (ASR)
on LRS2
Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR)
+7
Recurrent Inference in Text Editing
In neural text editing, prevalent sequence-to-sequence based approaches directly map the unedited text either to the edited text or the editing operations, in which the performance is degraded by the limited source text encoding and long, varying decoding steps.
Natural Language Inference over Interaction Space
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
Ranked #12 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
Ruminating Reader: Reasoning with Gated Multi-Hop Attention
To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context.
Ranked #36 on
Question Answering
on SQuAD1.1 dev
