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Found 15 papers, 2 papers with code
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
Moreover, we propose the Representation-of-Thought (RoT) framework, which leverages the robustness of low-dimensional representation spaces to enhance the robustness of the reasoning process in CoTs.
MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language
Later for each sub-problem, it iteratively queries the external memory and/or target LLM in order to generate the final response.
A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning
Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning performance for large language models (LLMs).
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
To address these issues, in this paper, we propose a novel framework named RULE-KE, i. e., RULE based Knowledge Editing, which is a cherry on the top for augmenting the performance of all existing MQA methods under KE.
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
We also propose GSM8K-aug, i. e., an extended version of the existing GSM8K benchmark for task-agnostic prompts in order to provide a comprehensive evaluation platform.
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.
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.
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.
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.
