activity
20242026
most citedReFT: Representation Finetuning for Language Models

4 citations · 7 across the 14 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

5 papers · 2 filters

cs.CL2025★ 1 cited

Transcribe, Translate, or Transliterate: An Investigation of Intermediate Representations in Spoken Language Models

Tolúlopé Ògúnrèmí, Christopher D. Manning, Dan Jurafsky +1

Spoken language models (SLMs) that integrate speech with large language models (LMs) rely on modality adapters (MAs) to map the output of speech encoders to a representation that i…

cs.CL2025

False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models

Julie Kallini, Dan Jurafsky, Christopher Potts +1

Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages. Does token overlap facilitate cross-lingual transfer or instead introduce…

cs.CL2025★ 1 cited

From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning

Chen Shani, Liron Soffer, Dan Jurafsky +2

Humans organize knowledge into compact conceptual categories that balance compression with semantic richness. Large Language Models (LLMs) exhibit impressive linguistic abilities,…

cs.CL2025

Mechanistic evaluation of Transformers and state space models

Aryaman Arora, Neil Rathi, Nikil Roashan Selvam +3

State space models (SSMs) for language modelling promise an efficient and performant alternative to quadratic-attention Transformers, yet show variable performance on recalling bas…

cs.CL2025

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Zhengxuan Wu, Aryaman Arora, Atticus Geiger +5

Fine-grained steering of language model outputs is essential for safety and reliability. Prompting and finetuning are widely used to achieve these goals, but interpretability resea…