From the 1 of 104 linked papers with an AI index.
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Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
Jonas Rohweder, Subhabrata Dutta, Iryna Gurevych
The paper argues that phenomena such as induction heads, function vectors, and the Hydra effect in Transformer language models can be explained by hierarchical latent structures in…
To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias
Federico Marcuzzi, Xuefei Ning, Roy Schwartz +1
As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literature suffers from wide…
Judgment-Grounded Expansion for Peer Review Generation
Sheng Lu, Lizhen Qu, Iryna Gurevych
Automatic review generation is a promising direction for accelerating scientific progress. While most work adopts an end-to-end setup, its fully automated nature makes it less suit…
Patches of Nonlinearity: Instruction Vectors in Large Language Models
Irina Bigoulaeva, Jonas Rohweder, Subhabrata Dutta +1
Despite the recent success of instruction-tuned language models and their ubiquitous usage, very little is known of how models process instructions internally. In this work, we add…
ClaimFlow: Tracing the Evolution of Scientific Claims in NLP
Aniket Pramanick, Yufang Hou, Saif M. Mohammad +1
Scientific papers advance that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of…
From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
Haishuo Fang, Yue Feng, Iryna Gurevych
Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete…