3 citations · 5 across the 17 of their papers we have counts for
17 papers
COMPKE: Complex Question Answering under Knowledge Editing
Keyuan Cheng, Zijian Kan, Zhixian He +5
Knowledge Editing, which efficiently modifies the knowledge in large language models, has gathered great attention. Current benchmarks primarily use multi-hop question answering to…
From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary
Qirui Zheng, Xingbo Wang, Keyuan Cheng +5
The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding research area, offering advantages such as scalable availability…
CODEMENV: Benchmarking Large Language Models on Code Migration
Keyuan Cheng, Xudong Shen, Yihao Yang +6
Large language models (LLMs) have shown remarkable capabilities across various software engineering tasks; however, their effectiveness in code migration, adapting code to run in d…
Quasi-Orthogonality and Polysemy: The Limits of Quantum Analogy in Large Language Models
Karl Svozil
We analyze two geometric aspects of meaning-sensitive behavior in next-token models. First, high-dimensional geometry permits exponentially many nearly orthogonal feature direction…
Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models
Shiran Dudy, Thulasi Tholeti, Resmi Ramachandranpillai +3
Recent advancements in Large Language Models (LLMs) have made them a popular information-seeking tool among end users. However, the statistical training methods for LLMs have raise…
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
Lijie Hu, Liang Liu, Shu Yang +5
Large Language Models have demonstrated remarkable abilities across various tasks, with Chain-of-Thought (CoT) prompting emerging as a key technique to enhance reasoning capabiliti…
MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language
Muhammad Asif Ali, Nawal Daftardar, Mutayyaba Waheed +2
Large Language Models (LLMs) have demonstrated significant capabilities across numerous application domains. A key challenge is to keep these models updated with latest available i…
A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning
Lijie Hu, Liang Liu, Shu Yang +6
Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning performance for large language models (LLMs). While some studies focus on improving CoT accuracy throug…
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
Keyuan Cheng, Muhammad Asif Ali, Shu Yang +7
Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in Large Language Models (LLMs). While best-performing solutions in this domain use a plan and so…
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Muhammad Asif Ali, Zhengping Li, Shu Yang +8
Large Language Models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks. While prompting is a crucial tool for LLM inference, we obse…
Multi-hop Question Answering under Temporal Knowledge Editing
Keyuan Cheng, Gang Lin, Haoyang Fei +5
Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models. However, existing models for MQA under KE ex…
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
Shu Yang, Jiayuan Su, Han Jiang +5
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. While exi…
MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions
Shu Yang, Muhammad Asif Ali, Lu Yu +2
The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics. However, there exists…
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
Shu Yang, Muhammad Asif Ali, Cheng-Long Wang +2
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge. In this paper, we propose MoRAL, i.e., Mixtur…
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)
Muhammad Asif Ali, Yan Hu, Jianbin Qin +1
Antonyms vs synonyms distinction is a core challenge in lexico-semantic analysis and automated lexical resource construction. These pairs share a similar distributional context whi…
GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings
Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin +2
Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces. Existing attempts aimed at controlling the relat…
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
Muhammad Asif Ali, Yan Hu, Jianbin Qin +1
Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation. The end performance of these dictionaries relies upo…