Papers by Xiajie Zhang
CRPS: Curriculum Replay via Progressive Suffixes from Successful Trajectories for Long-Horizon LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Long-horizon agents trained with sparse terminal rewards experience slow and unstable learning . this is amplified by group-normalized on-policy objectives, which yield degenerate advantages and weak learning signals. |
| Approach: | They propose a lightweight RL-training strategy that turns terminal successes into within-trajectory curriculum by replaying from successful suffix states. |
| Outcome: | The proposed strategy outperforms full-episode GRPO and naive experience replay across ALFWorld and WebShop with different foundation models. |
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling (2024.acl-long)
Copied to clipboard
Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex Pentland, Yoon Kim, Deb Roy, Jad Kabbara
| Challenge: | a novel application of large language models (LLMs) to legal education helps non-experts learn complex legal concepts . authors find storytelling helps nonexperts understand complex legal terms and concepts compared to definitions . |
| Approach: | They propose a novel application of large language models to legal education . they use LLMs to generate legal stories explaining complex legal concepts . |
| Outcome: | The proposed method improves comprehension and interest among non-native speakers compared to definitions . the novel method also shows that non-experts retain more stories . |
PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have shown that LLMs can generate content that aligns with their assigned personality traits, but there is limited research on whether they consistently reflect specific personality traits. |
| Approach: | They propose to study the behavior of LLM-based agents which they refer to as LLM personas and simulate them to measure their personality traits. |
| Outcome: | The proposed model is based on the Big Five personality model and has been validated by human evaluations and automatic evaluations. |