Papers by Qiong Wu
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods employ sentence-level retrieval and fusion methods, which may lead to similarity bias and interference from irrelevant information in unstructured knowledge sentences. |
| Approach: | They propose a segment-level and category-oriented network to solve similarity bias problem by segmenting and prompting knowledge retrieval methods and a category-based grounding method. |
| Outcome: | The proposed model eliminates similarity bias and improves the overall performance of the KB-REC task. |
SEC-FinTables: Evaluating Large Language Models for Detecting Logical Inconsistencies on Tabular Data (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are increasingly deployed in high-stakes domains where logical inconsistencies are unrecognized. |
| Approach: | They propose a benchmarking system that decomposes inconsistency detection into granular subtasks and a protocol that decompiles it into subtask. |
| Outcome: | The proposed model decomposes inconsistencies into subtasks and identifies them in 103,395 real-world and error-injected table instances. |
Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limited generalization. |
| Approach: | They propose a framework that decouples entity identification from section-level re-ranking. |
| Outcome: | The proposed framework outperforms fine-tuned multi-modal re-ranking baselines while reducing training and inference complexity. |
Walk in Others’ Shoes with a Single Glance: Human-Centric Visual Grounding with Top-View Perspective Transformation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing VLMs are insensitive to information differences induced by slight perspective changes. |
| Approach: | They propose a visual perspective-taking task that requires robots to interpret human-centric instructions and identify corresponding objects from robot perspectives. |
| Outcome: | The proposed method improves performance by up to 18% and generalizes effectively to robotic and dynamic scenarios. |
Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation methods rely on rule-based matching with shallow semantic understanding or adopt LLM-as-a-Judge approaches that incur high cost and latency while offering limited error interpretability. |
| Approach: | They propose a curriculum learning based hierarchical framework for QA task evaluation that supports quick scoring and fine-grained error analysis. |
| Outcome: | The proposed framework outperforms baseline methods on quick scoring and error analysis tasks while being 25 faster. |
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
| Approach: | They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks. |
| Outcome: | The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings. |