895 citations · 2.1k across the 169 of their papers we have counts for
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The Boundaries of Automation: A Theory of Persistent Human Participation
Fares Fourati, Hinrich Schütze, Eyke Hüllermeier +1
The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the a…
Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models
Zhiqing Yang, Yilun Liu, Yunpu Ma +2
Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains underexplored. We investigate this…
Output Vector Editing for Memorization Mitigation in Large Language Models
Ahmad Dawar Hakimi, Kaiwei Lei, Isabelle Augenstein +1
Large language models can reproduce training text, creating privacy, copyright, and security risks. We propose Output Vector Editing (OVE), a post-hoc method for suppressing text c…
Edit the Bits, Diff the Codes: Bitwise Residual Editing for Visual Autoregressive Models
Shengqiang Zhang, Ruotong Liao, Volker Tresp +2
Text-guided image editing with visual autoregressive (VAR) generators requires controlling both what the model samples and where the sampled change is written back into the image c…
Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?
Renhao Pei, Yihong Liu, Sampo Pyysalo +2
Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context le…
Expert-Aware Causal Tracing of Factual Recall in Sparse MoE Language Models
Yuetian Lu, Ali Modarressi, Yihong Liu +1
Activation patching can identify a mixture-of-experts (MoE) block whose clean output restores a corrupted factual prediction. However, because the block output combines contributio…
ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning
Yongkang Liu, Zijing Wang, Mengjie Zhao +7
This work presents \textsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activated working set. \textsc{Chunk…
SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
Yongkang Liu, Xing Li, Mengjie Zhao +7
As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation…
GKnow: Measuring the Entanglement of Gender Bias and Factual Gender
Leonor Veloso, Hinrich Schütze
Recent works have analyzed the impact of individual components of neural networks on gendered predictions, often with a focus on mitigating gender bias. However, mechanistic interp…
Why Better Cross-Lingual Alignment Fails for Better Cross-Lingual Transfer: Case of Encoders
Yana Veitsman, Yihong Liu, Hinrich Schütze
Cross-lingual alignment is often assumed to improve cross-lingual transfer by bringing representations of different languages closer together. However, improvements in representati…