1 citations · 1 across the 4 of their papers we have counts for
5 papers
Diffusion Language Models are Provably Optimal Parallel Samplers
Haozhe Jiang, Nika Haghtalab, Lijie Chen
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundati…
Understanding In-context Learning of Addition via Activation Subspaces
Xinyan Hu, Kayo Yin, Michael I. Jordan +2
To perform few-shot learning, language models extract signals from a few input-label pairs, aggregate them into a learned prediction rule, and apply this rule to new inputs. How is…
Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations
Yiyou Sun, Yu Gai, Lijie Chen +3
Large language models (LLMs) frequently generate hallucinations-content that deviates from factual accuracy or provided context-posing challenges for diagnosis due to the complex i…
Theoretical limitations of multi-layer Transformer
Lijie Chen, Binghui Peng, Hongxun Wu
Transformers, especially the decoder-only variants, are the backbone of most modern large language models; yet we do not have much understanding of their expressive power except fo…
LatentQA: Teaching LLMs to Decode Activations Into Natural Language
Alexander Pan, Lijie Chen, Jacob Steinhardt
Top-down transparency typically analyzes language model activations using probes with scalar or single-token outputs, limiting the range of behaviors that can be captured. To allev…