6 papers · 1 filter
Why Mean Pooling Works: Quantifying Second-Order Collapse in Text Embeddings
Tomomasa Hara, Hiroto Kurita, Masaaki Imaizumi +2
For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach. This paper examines whether mean pooling actually works well in real mode…
Sycophancy Hides Linearly in the Attention Heads
Rifo Genadi, Munachiso Nwadike, Nurdaulet Mukhituly +3
We find that correct-to-incorrect sycophancy signals are most linearly separable within multi-head attention activations. Motivated by the linear representation hypothesis, we trai…
Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
Nhi Hoai Doan, Tatsuya Hiraoka, Kentaro Inui
This paper investigates the relationship between large language models' (LLMs) ability to recognize repetitive input patterns and their performance on in-context learning (ICL). In…
Emergence of Primacy and Recency Effect in Mamba: A Mechanistic Point of View
Muhammad Cendekia Airlangga, Hilal AlQuabeh, Munachiso S Nwadike +1
We study memory in state-space language models using primacy and recency effects as behavioral tools to uncover how information is retained and forgotten over time. Applying struct…
Spelling-out is not Straightforward: LLMs' Capability of Tokenization from Token to Characters
Tatsuya Hiraoka, Kentaro Inui
Large language models (LLMs) can spell out tokens character by character with high accuracy, yet they struggle with more complex character-level tasks, such as identifying composit…
Number Representations in LLMs: A Computational Parallel to Human Perception
H. V. AlquBoj, Hilal AlQuabeh, Velibor Bojkovic +4
Humans are believed to perceive numbers on a logarithmic mental number line, where smaller values are represented with greater resolution than larger ones. This cognitive bias, sup…