1 citations · 1 across the 8 of their papers we have counts for
11 papers
How Does Post-Training Shape LLM Value Preferences?
Yang Liu, Shaoyang Xu, Masahiro Kaneko +3
During post-training, large language models (LLMs) learn value preferences from corpora that may contain imbalanced proportions of opinions associated with different demographic gr…
Silicon Samples Match the Answers but Not the Respondents
Yusuke Kumagae, Masahiro Kaneko
Silicon sampling generates survey respondents by having a large language model (LLM) answer a survey as a person with given demographic attributes, and the resulting synthetic popu…
Speak vs. Know: Balancing Behavior and Knowledge Distributions of Large Language Models
Yang Liu, Masahiro Kaneko, Chenhui Chu
Recent work reports that the distribution verbalized directly in the output text aligns far better with human opinion distributions than the next-token log-probability distribution…
In-Context Learning as Implicit Policy Gradient
Masahiro Kaneko, Timothy Baldwin
Recent work has shown that large language models (LLMs) can iteratively improve their outputs by incorporating generated samples and their corresponding evaluation scores as in-con…
EconSimulacra: A Digital Twin Platform of Socio-Economic Systems Powered by LLM Agents
Ryuji Hashimoto, Masahiro Kaneko, Kentaro Ueda +2
Real-world social behavior emerges from tightly coupled domains: economic conditions shape mobility and social interactions, while online attention and offline activity feed back i…
From Heard to Lived Opinions: Simulating Opinion Dynamics with Grounded LLM Agents in Economic Environments
Ryuji Hashimoto, Masahiro Kaneko, Ryosuke Takata +2
Opinion dynamics (OD) studies how individual opinions evolve and generate collective patterns such as consensus and polarization. While recent work explores OD using populations of…
Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation
Harry Stuart, Masahiro Kaneko, Timothy Baldwin
Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g.\ interviews conducte…
JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks
Masahiro Kaneko, Ayana Niwa, Timothy Baldwin
Fake news undermines societal trust and decision-making across politics, economics, health, and international relations, and in extreme cases threatens human lives and societal saf…
Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization
Masahiro Kaneko, Zeerak Talat, Timothy Baldwin
Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide e…
Bits Leaked per Query: Information-Theoretic Bounds on Adversarial Attacks against LLMs
Masahiro Kaneko, Timothy Baldwin
Adversarial attacks by malicious users that threaten the safety of large language models (LLMs) can be viewed as attempts to infer a target property that is unknown when an ins…
A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish
Masahiro Kaneko, Timothy Baldwin
Large Language Models (LLMs) are trained on massive web-crawled corpora. This poses risks of leakage, including personal information, copyrighted texts, and benchmark datasets. Suc…