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Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
Richard Antonello, Chandan Singh, Shailee Jain +5
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is uncl…
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
Yanda Chen, Chandan Singh, Xiaodong Liu +4
Large language models (LLMs) often generate convincing, fluent explanations. However, different from humans, they often generate inconsistent explanations on different inputs. For…
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Qingru Zhang, Chandan Singh, Liyuan Liu +4
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the rea…