Papers by Weizhe Lin
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models (2025.findings-naacl)
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Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, Lin Yan
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across various domains since the release of ChatGPT . a key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. |
| Approach: | They introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling to efficiently find good responses by promoting diversity. |
| Outcome: | The proposed method enhances inference-time generation quality and benefits training in the alignment stage. |
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing models for dialogue state tracking are based on Graph Attention Networks . if the relationship between slots and values is modelled explicitly, this can be improved . |
| Approach: | They propose a model architecture that augments GPT-2 with Graph Attention Networks to allow sequential prediction of slot values. |
| Outcome: | The proposed architecture improves performance against a strong GPT-2 baseline and with sparsely supervised training. |
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection (2025.emnlp-main)
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| Challenge: | Large Multimodal Models (LMMs) have shown promise in hateful meme detection, but they face limitations like sub-optimal performance and limited out-of-domain generalization capabilities. |
| Approach: | They propose a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. |
| Outcome: | The proposed framework outperforms larger agentic systems in detecting hateful memes under adversarial attacks while maintaining the general vision-language capabilities of LMMs. |
No, you’re not alone: A better way to find people with similar experiences on Reddit (D19-55)
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| Challenge: | a probabilistic clustering algorithm can help users find posts that discuss experiences similar to their own . a recent study shows that probabilistic Clustering can yield a better performance than baseline clustering methods . |
| Approach: | They propose a probabilistic clustering algorithm that can help Reddit users find posts that discuss experiences similar to their own. |
| Outcome: | The proposed algorithm can find posts that discuss experiences similar to their own . it performs better than baseline clustering methods due to high runtime overhead . |
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)
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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen
| Challenge: | Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings. |
| Approach: | X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans . |
| Outcome: | X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data . |
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking (2023.findings-eacl)
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| Challenge: | Pre-trained language models (PLMs) can track only slots drawn from a database or domain ontology. |
| Approach: | They propose a framework for generating synthetic schemas which uses tree-based ranking to optimise lexical diversity and semantic faithfulness. |
| Outcome: | The proposed framework improves the generalisation of strong baselines by augmenting training data with prompts generated by the framework. |
Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning (2024.acl-long)
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| Challenge: | Existing systems for detecting hateful memes lack sensitivity to subtle differences in memes that are vital for correct hatefulness classification. |
| Approach: | They propose to construct a hatefulness-aware embedding space through retrieval-guided contrastive training to identify hatefulness based on data unseen in training. |
| Outcome: | The proposed system outperforms existing models on the HatefulMemes dataset with an AUROC of 87.0 and improves contextual understanding across domains. |
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering (2023.findings-eacl)
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| Challenge: | Fact-based Visual Question Answering (FVQA) is a visual question answering task that requires information retrieval using common sense knowledge graphs to answer. |
| Approach: | They propose a new test question with adversarial variants to address this imbalance by using a KB-VQA dataset that is small and contains only one answer per question. |
| Outcome: | The proposed version reduces the vulnerability of the original FVQA dataset without human annotations. |
Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) remain vulnerable to jailbreak attacks due to evolving nature and diversity of attack strategies. |
| Approach: | They propose a framework for jailbreak detection that integrates a database of known attack examples into Retrieval-Augmented Generation to infer the underlying, malicious user query and jailbreak strategy used to attack the system. |
| Outcome: | The proposed framework reduces the effectiveness of strong jailbreak attacks while maintaining low rejection rates for benign queries. |
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers (2024.acl-long)
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| Challenge: | Large Multimodal Models excel in natural language and visual understanding but are challenged by challenging tasks such as Knowledge-based Visual Question Answering (KB-VQA). |
| Approach: | They propose a framework for training Large Multimodal Models (LMMs) to perform KB-VQA tasks. |
| Outcome: | The proposed framework is used to train and evaluate multi-modal retrievers. |
Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata (2024.naacl-short)
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| Challenge: | Existing non-autoregressive (NAR) models fail to generate specified entity names in up to 40% of responses and produce OOV errors. |
| Approach: | They propose a constrained decoding algorithm for Directed Acyclic T5 model which offers lexical, vocabulary and length control. |
| Outcome: | The proposed model significantly improves on Schema Guided Dialogue and DART datasets, establishing strong results for Task-Oriented Dialog and Data-to-Text NLG. |
An Inner Table Retriever for Robust Table Question Answering (2023.acl-long)
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| Challenge: | Table Question Answering (TableQA) is a task of answering NL user questions using factoid answers extracted from table content. |
| Approach: | They propose a method for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. |
| Outcome: | The proposed method can improve TableQA's accuracy with up to 1.3-4.8% and achieve state-of-the-art in two benchmarks. |
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding (2024.naacl-short)
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| Challenge: | Recent work shows that MBR decoding can significantly improve translation performance of Multilingual Large Language Models. |
| Approach: | They propose a method that uses a monolingual fine-tuning set to fine- tune MLLMs to get the gains of MBR without additional computation in inference. |
| Outcome: | The proposed method outperforms greedy decoding and beam search on multiple NMT tests. |
LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering (2023.acl-short)
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| Challenge: | Recent open-domain TableQA pipelines use a combination of retriever and reader . a table can be very large and might contain heterogeneous information across rows/columns . |
| Approach: | They propose to combine a retriever-reader pipeline with a binary relevance token to train the retriever and reader. |
| Outcome: | The proposed approaches improve on two open-domain TableQA datasets. |
Retrieval Augmented Visual Question Answering with Outside Knowledge (2022.emnlp-main)
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| Challenge: | Recent systems that use Dense Passage Retrieval (DPR) for visual question answering use Denses trained separately from answer generation. |
| Approach: | They propose a joint training scheme that integrates DPR with answer generation to improve the performance of OK-VQA systems. |
| Outcome: | The proposed model outperforms existing systems with strong Dense Passage Retrieval (DPR) it yields significant improvements in answer quality and computation required for training. |
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)
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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong
| Challenge: | Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. |
| Approach: | They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs. |
| Outcome: | The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks. |