74 citations · 136 across the 10 of their papers we have counts for
10 papers
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
Hengyuan Zhang, Zhihao Zhang, Mingyang Wang +26
Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat…
Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs
Zeping Yu, Sophia Ananiadou
Although multimodal large language models (MLLMs) have achieved impressive performance, the multimodal instruction tuning stage often causes catastrophic forgetting of the base LLM…
Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models
Zeping Yu, Yonatan Belinkov, Sophia Ananiadou
We investigate how large language models perform latent multi-hop reasoning in prompts like "Wolfgang Amadeus Mozart's mother's spouse is". To analyze this process, we introduce lo…
Understanding and Mitigating Gender Bias in LLMs via Interpretable Neuron Editing
Zeping Yu, Sophia Ananiadou
Large language models (LLMs) often exhibit gender bias, posing challenges for their safe deployment. Existing methods to mitigate bias lack a comprehensive understanding of its mec…
Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question Answering
Zeping Yu, Sophia Ananiadou
Understanding the mechanisms behind Large Language Models (LLMs) is crucial for designing improved models and strategies. While recent studies have yielded valuable insights into t…
Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis
Zeping Yu, Sophia Ananiadou
We find arithmetic ability resides within a limited number of attention heads, with each head specializing in distinct operations. To delve into the reason, we introduce the Compar…
How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning
Zeping Yu, Sophia Ananiadou
We investigate the mechanism of in-context learning (ICL) on sentence classification tasks with semantically-unrelated labels ("foo"/"bar"). We find intervening in only 1\% heads (…
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis
Zhiwei Liu, Kailai Yang, Tianlin Zhang +2
Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of LL…
Neuron-Level Knowledge Attribution in Large Language Models
Zeping Yu, Sophia Ananiadou
Identifying important neurons for final predictions is essential for understanding the mechanisms of large language models. Due to computational constraints, current attribution te…
Emotion Detection for Misinformation: A Review
Zhiwei Liu, Tianlin Zhang, Kailai Yang +3
With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and ru…