16 citations · 34 across the 14 of their papers we have counts for
15 papers
A Capability Broker for Workflow-Network QoS Coordination in B5G/6G Industrial Services
Qize Guo, Yan Chen, Taleb Tarik +3
Industrial services in beyond-fifth-generation (B5G) and sixth-generation (6G) networks are increasingly executed as multi-phase workflows whose quality-of-service (QoS) demands ch…
Navigating by Old Maps: The Pitfalls of Static Mechanistic Localization in LLM Post-Training
Hang Chen, Jiaying Zhu, Hongyang Chen +3
The "Locate-then-Update" paradigm has become a predominant approach in the post-training of large language models (LLMs), identifying critical components via mechanistic interpreta…
CLUE: Conflict-guided Localization for LLM Unlearning Framework
Hang Chen, Jiaying Zhu, Xinyu Yang +1
The LLM unlearning aims to eliminate the influence of undesirable data without affecting causally unrelated information. This process typically involves using a forget set to remov…
Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates
Hang Chen, Jiaying Zhu, Xinyu Yang +1
Circuit discovery has gradually become one of the prominent methods for mechanistic interpretability, and research on circuit completeness has also garnered increasing attention. M…
Skill Path: Unveiling Language Skills from Circuit Graphs
Hang Chen, Jiaying Zhu, Xinyu Yang +1
Circuit graph discovery has emerged as a fundamental approach to elucidating the skill mechanistic of language models. Despite the output faithfulness of circuit graphs, they suffe…
Quantifying Semantic Emergence in Language Models
Hang Chen, Xinyu Yang, Jiaying Zhu +1
Large language models (LLMs) are widely recognized for their exceptional capacity to capture semantics meaning. Yet, there remains no established metric to quantify this capability…
Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets
Hang Chen, Xinyu Yang, Keqing Du
Integrating deep learning and causal discovery has encouraged us to spot that learning causal structures and representations in dialogue and video is full of challenges. We defined…
A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations
Hang Chen, Keqing Du, Chenguang Li +1
The fusion of causal models with deep learning introducing increasingly intricate data sets, such as the causal associations within images or between textual components, has surfac…
CASR: Refining Action Segmentation via Marginalizing Frame-levle Causal Relationships
Keqing Du, Xinyu Yang, Hang Chen
Integrating deep learning and causal discovery has increased the interpretability of Temporal Action Segmentation (TAS) tasks. However, frame-level causal relationships exist many…
SSL Framework for Causal Inconsistency between Structures and Representations
Hang Chen, Xinyu Yang, Keqing Du +1
The cross-pollination between causal discovery and deep learning has led to increasingly extensive interactions. It results in a large number of deep learning data types (such as i…
Learning a Structural Causal Model for Intuition Reasoning in Conversation
Hang Chen, Bingyu Liao, Jing Luo +2
Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models including Large Language Model. Conversation reasoning, as a critical component…
Towards Causal Representation Learning and Deconfounding from Indefinite Data
Hang Chen, Xinyu Yang, Qing Yang
Owing to the cross-pollination between causal discovery and deep learning, non-statistical data (e.g., images, text, etc.) encounters significant conflicts in terms of properties a…
How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
Hang Chen, Jing Luo, Xinyu Yang +1
Our investigation into the Affective Reasoning in Conversation (ARC) task highlights the challenge of causal discrimination. Almost all existing models, including large language mo…
A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms
Hang Chen, Keqing Du, Xinyu Yang +1
Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relat…
Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction
Hang Chen, Xinyu Yang, Xiang Li
Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract potential pairs of emotions and corresponding causes from documents. Previous approaches have focused on…