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Danushka Bollegala

ACL 2026×3ICLR 2026×2社会与人本数据与预训练推理可解释性代码
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核心/参与34
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0 一作 · 5 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

5
作者位次 #3/4ICLR 2026Poster· Social Aspects / Accountability, Transparency and Interpretability

Neuron-Level Analysis of Cultural Understanding in Large Language Models

Taisei Yamamoto · Ryoma Kumon · Danushka Bollegala · Hitomi Yanaka

可解释性社会与人本OpenReview
作者位次 #2/2ACL 2026Poster· Natural Language Processing

Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy

Gaifan Zhang · Danushka Bollegala

摘要

We propose a method to compare and visualise sentence encoders at scale by creating a map of encoders where each sentence encoder is represented in relation to the other sentence encoders. Specifically, we first represent a sentence encoder using an embedding matrix of a sentence set, where each...

多模态数据与预训练检索与知识OpenReview 项目页
作者位次 #3/3ACL 2026Poster· Natural Language Processing

Synthetic Data Generation for Training Diversified Commonsense Reasoning Models

Tianhui Zhang · Bei Peng · Danushka Bollegala

摘要

Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering multiple plausible alternative scenarios, reflecting the diversity in their responses. Despite the growing need to train diverse commonsense generator...

数据与预训练推理智能体与工具OpenReview 项目页
作者位次 #2/3ACL 2026Poster· Natural Language Processing

A Multilingual Social Bias Benchmark Incorporating Thinking Processes

Masahiro Kaneko · Danushka Bollegala · Timothy Baldwin

摘要

Large Language Models (LLMs) can learn both useful knowledge and harmful stereotypes, making bias evaluation essential.Existing frameworks fall into two types: those considering reasoning steps (Thinking Process-Aware Evaluation, TPAE) and those focusing only on final outputs (Straight-to-the-Ans...

社会与人本评测与基准推理OpenReview 项目页

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