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Found 8 papers, 6 papers with code
Offset Unlearning for Large Language Models
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as copyrighted, harmful, and private content has led to ethical and legal concerns.
Contrastive Instruction Tuning
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks.
DeAL: Decoding-time Alignment for Large Language Models
Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF).
Robust Natural Language Understanding with Residual Attention Debiasing
However, previous ensemble-based debiasing methods typically apply debiasing on top-level logits without directly addressing biased attention patterns.
Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations
It is unclear whether the compositional semantics of sentences can be directly reflected as compositional operations in the embedding space.
Parameter-Efficient Tuning with Special Token Adaptation
Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks.
Unified Semantic Typing with Meaningful Label Inference
Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types.
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Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models
In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models.
