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Found 11 papers, 2 papers with code
PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Large language models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks.
Multi-hop Question Answering under Temporal Knowledge Editing
Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models.
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial.
MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions
The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics.
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge.
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)
In this paper, we propose InterlaCed Encoder NETworks (i. e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner.
GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings
Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces.
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation.
FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space
Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context.
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations
Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP).
Antonym-Synonym Classification Based on New Sub-space Embeddings
We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper.
