Papers by Jia Su
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework (2023.acl-long)
Copied to clipboard
| Challenge: | Current evaluations of FEC models that depend on factuality metrics are not reliable and detailed enough. |
| Approach: | They propose a fine-grained evaluation framework that automatically evaluates FEC models on different error categories. |
| Outcome: | The proposed evaluation framework compares models on different error categories and finds the best training modes and significant differences in the performance of existing models. |
KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models (2023.acl-industry)
Copied to clipboard
| Challenge: | Image ad understanding is a crucial task with wide real-world applications, but is under-explored in the machine learning community due to the lack of foundational vision-language models (VLMs) . |
| Approach: | They propose a simple feature adaptation strategy to fuse multimodal information for image ads and further empower it with knowledge of real-world entities. |
| Outcome: | The proposed strategy fuses multimodal information for image ads and empowers it with knowledge of real-world entities. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance. |
| Approach: | They propose a framework that frames alignment as a conditional capacity separation problem. |
| Outcome: | The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models. |
From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at the Repository Level (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for repository-level reasoning are inconsistent . repoReason is a white-box diagnostic benchmark centered on abductive assertion verification . |
| Approach: | They propose a white-box diagnostic benchmark centered on abductive assertion verification. |
| Outcome: | The proposed framework eliminates memorization while maintaining authentic logical depth . it also regenerates ground-truth states and quantifyes reasoning via three orthogonal metrics . |
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)
Copied to clipboard
Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu
| Challenge: | Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework . |
| Approach: | They propose a training-free inference framework that simulates a metacognitive self-correction process. |
| Outcome: | The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE. |
MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for AI math tutoring largely overlook these skills. |
| Approach: | They evaluate 12 leading multimodal large language models and find clear performance gaps between them. |
| Outcome: | The proposed benchmarks show that they can solve 770 problems and provide diagnostics and guidance to students step by step. |
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)
Copied to clipboard
Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo
| Challenge: | Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. |
| Approach: | They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities. |
| Outcome: | The proposed bilingual benchmark assesses models’ language understanding and generation capabilities. |
Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on white-box attacks focus on black-box LLMs, leaving black- box scenarios underexplored. |
| Approach: | They propose an automated algorithm designed for black-box LLMs that constructs the DoS Attack Tree and expands the node coverage to achieve effectiveness under black- box conditions. |
| Outcome: | The proposed algorithm can be used to build a DoS Attack Tree and expand the node coverage to achieve effectiveness under black-box conditions. |