Papers by Jianda Chen
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge. |
| Approach: | They propose a dataset that includes 24204 instances where each instance interprets the LLM’s reasoning behavior using knowledge graphs and graph attention networks (GAT). |
| Outcome: | The proposed explanation framework reduces hallucinations and improves grounded explanation generation in large language models. |
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. |
| Approach: | They propose a tri-encoder sequential retriever that models a Markov Decision Process (MDP) this method decomposes the probability of retrieving a set of elements into a sequence of conditional probabilities and allows each retrieval step to be conditioned on previously selected examples. |
| Outcome: | The proposed method outperforms baselines and shows that it can handle multiple pieces of evidence or examples. |
From Tasks to Teams: A Risk-First Evaluation Framework for Multi-Agent LLM Systems in Finance (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on task specific metrics such as accuracy, F1 score, or ROUGE. |
| Approach: | They propose a multi-agent, safety-aware evaluation agent that audits large language models without fine-tuning. |
| Outcome: | M-SAEA identifies unsafe trajectories with minimal false positives and reveals latent risks that are not addressed by standard metrics. |