arXiv AI By Shuhe Wang, Lachlan Cowley, Eduard Hovy, Jey Han Lau

AutoLexSteer: Automatic Contrast Construction for Lexical Activation Steering

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arXiv Machine Learning
Sep 10

Disentangling Steering Vectors

arXiv:2609.07037v1 Announce Type: new Abstract: Activation steering has emerged as a lightweight, inference-time approach to control the behavior of Large Language Models (LLMs). However, traditional...

By Takeru Hiramatsu, Kyohei Atarashi, Koh Takeuchi, Hisashi Kashima
arXiv Machine Learning
Jun 11

When is Your LLM Steerable?

arXiv:2606. 11599v1 Announce Type: cross Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration.

By Chenrui Fan, Yize Cheng, Ming Li, Soheil Feizi, Tianyi Zhou
arXiv Computation and Language
Sep 2

CWoMP: Morpheme Representation Learning for Interlinear Glossing

CWoMP (Contrastive Word‑Morpheme Pretraining) is a new approach for generating interlinear glossed text that treats morphemes as atomic form‑meaning units with learned representations. It uses a contrastively trained encoder to align words in context with their constituent morphemes in a shared embedding space, and an autoregressive decoder that retrieves morpheme sequences from a mutable lexicon of these embeddings. The method yields interpretable predictions grounded in lexicon entries and allows users to improve results at inference time by expanding the lexicon without retraining, achieving superior performance and efficiency on diverse low‑resource languages, especially in extremely low‑resource settings.

By Morris Alper, Enora Rice, Bhargav Shandilya, Alexis Palmer, Lori Levin