Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

sentiment control

Sentiment control is a technique in natural language processing and text generation that directs a language model to produce output with a specific emotional tone or polarity, such as positive, negative, or neutral. It operates by guiding the generation process so the resulting text reflects a target sentiment while maintaining semantic coherence, grammatical fluency, and topical relevance. Implementations typically achieve this through methods such as steering vectors within the model embedding or activation space, adapter-based parameter tuning, reinforcement learning, or conditional decoding strategies. This capability allows systems to reliably modulate the attitude or tone of generated content across applications like dialogue agents, creative writing tools, and customer support interfaces.

1 item

Word Embeddings Are Steers for Language Models

Word Embeddings Are Steers for Language Models

Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek F. Abdelzaher, Heng Ji

Why you should read this

Proposes LM-Steer, a lightweight approach that applies linear transformations to output word embeddings to achieve parameter-efficient, transferable, and continuous style control across language models without retraining internal weights.

Recent studies have shown that large language models (LLMs), when augmented with additional weights through adapters—for instance, LoRA—can learn specialized tasks without updating model parameters—a paradigm known as either Instruction Tuning (IT) for individual instructions or Task Arithmetic (TA) for task-specific adaptation. In such scenarios, different instruction/task datasets induce distinct steering vectors within LLMs' embedding space, shaping behavior according to downstream objectives. Inspired by recent advances in model merging or parameter composition, we investigate whether multiple steering directions (each optimized toward generating responses on one dataset while discouraging outputs outside its distribution via unlearning-like objectives) can be combined into a single vector enabling zero-shot generalization across diverse tasks.

Added

2026-10-02