3 citations · 8 across the 23 of their papers we have counts for
8 papers · 1 filter
TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model
Weixian Waylon Li, Yftah Ziser, Yifei Xie +2
Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub…
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models
Zheng Zhao, Yftah Ziser, Shay B. Cohen
Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models that can solve various natural language processing…
PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning
Tingchen Fu, Mrinank Sharma, Philip Torr +3
Preference learning is a central component for aligning current LLMs, but this process can be vulnerable to data poisoning attacks. To address this concern, we introduce PoisonBenc…
einspace: Searching for Neural Architectures from Fundamental Operations
Linus Ericsson, Miguel Espinosa, Chenhongyi Yang +5
Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional str…
LeanReasoner: Boosting Complex Logical Reasoning with Lean
Dongwei Jiang, Marcio Fonseca, Shay B. Cohen
Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning. We use Lean, a theorem prov…
Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions
Clement Neo, Shay B. Cohen, Fazl Barez
Understanding the inner workings of large language models (LLMs) is crucial for advancing their theoretical foundations and real-world applications. While the attention mechanism a…
Large Language Models Relearn Removed Concepts
Michelle Lo, Shay B. Cohen, Fazl Barez
Advances in model editing through neuron pruning hold promise for removing undesirable concepts from large language models. However, it remains unclear whether models have the capa…
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?
Marcio Fonseca, Shay B. Cohen
In this work, we investigate the controllability of large language models (LLMs) on scientific summarization tasks. We identify key stylistic and content coverage factors that char…