66 citations · 108 across the 9 of their papers we have counts for
9 papers
Praxist: From Experimental Artifacts to Solution Lineages
Jin Li, Ahmed Murtadha, Zhiyu Wang +14
Autonomous R&D agents now write, run, and improve executable artifacts under automated evaluation-but largely as laboratory instruments: shown on curated benchmarks, with gains tha…
Why Attend to Everything? Focus is the Key
Hengshuai Yao, Xing Chen, Ahmed Murtadha +8
Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into pretrained models, they often degrade p…
GAIN: Multiplicative Modulation for Domain Adaptation
Hengshuai Yao, Xing Chen, Ahmed Murtadha +1
Adapting LLMs to new domains causes forgetting because standard methods (e.g., full fine-tuning, LoRA) inject new directions into the weight space. We show that forgetting is gover…
Thin Keys, Full Values: Reducing KV Cache via Low-Dimensional Attention Selection
Hengshuai Yao, Xing Chen, Ahmed Murtadha +1
Standard Transformer attention uses identical dimensionality for queries, keys, and values, yet these components serve different roles: queries and keys produce scalar attention we…
The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak +5
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has paralle…
Rank-Aware Negative Training for Semi-Supervised Text Classification
Ahmed Murtadha, Shengfeng Pan, Wen Bo +4
Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and the…
Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition
Jianlin Su, Ahmed Murtadha, Shengfeng Pan +5
Named entity recognition (NER) task aims at identifying entities from a piece of text that belong to predefined semantic types such as person, location, organization, etc. The stat…
ZLPR: A Novel Loss for Multi-label Classification
Jianlin Su, Mingren Zhu, Ahmed Murtadha +3
In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classificati…
BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis
Murtadha Ahmed, Bo Wen, Shengfeng Pan +3
Aspect-based sentiment analysis (ABSA) aims to associate a text with a set of aspects and infer their respective sentimental polarities. State-of-the-art approaches are built on fi…