Papers by Haoran Tu

4 papers
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)

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Challenge: Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages.
Approach: They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks.
Outcome: The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B).
Decoding LLM Personality Measurement: Forced-Choice vs. Likert (2025.findings-acl)

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Challenge: Recent research has focused on investigating the psychological characteristics of Large Language Models (LLMs), emphasizing the importance of comprehending their behavioral traits.
Approach: They evaluated six Large Language Models: Llama-3.1-8B, GLM-4-9B, Claude-3.5-sonnet, and Deepseek-V3 and used the forced-choice test to assess their personality traits.
Outcome: The forced-choice test is more reliable and more accurate than the likert scale and forced-CHOICE test results for LLMs' Big Five personality scores.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

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Challenge: Existing methods focus on knowledge and linguistic patterns of characters.
Approach: They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome: The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.
Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events (2026.acl-long)

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Challenge: A critical bottleneck is the lack of ground-truth human data to link personality traits to emotional shifts.
Approach: They propose a large-scale dataset to capture reader-based emotional variations across news, social media, and life narratives.
Outcome: The proposed model captures reader-based emotional variations across news, social media, and life narratives.

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