Papers by Yiqi Chen
Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering (2022.findings-emnlp)
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| Challenge: | Recent rise of conversational applications has promoted the development of conversation KBQA (ConvKBQA). |
| Approach: | They propose a framework to produce a full-fledged rewritten question based on conversation history and then reason the answer by existing single-turn KBQA models. |
| Outcome: | The proposed framework produces a full-fledged rewritten question based on the conversation history and reasoned the answer by existing single-turn KBQA models. |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions due to high computational costs and limited parameter access. |
| Approach: | They propose a lightweight framework that guides small language models to refine LLMs’ outputs through chain-of-thought correction. |
| Outcome: | The proposed framework improves the average format accuracy and content correctness of LLM outputs by 35.4% and 29.4%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines. |
A Multi-Task Approach for Improving Biomedical Named Entity Recognition by Incorporating Multi-Granularity information (2021.findings-acl)
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| Challenge: | Neural named entity recognition (BioNER) methods require large amount of annotated data, while the annotating BioNER datasets are often difficult to obtain and small in scale due to the limitations of privacy, ethics and high degree of specialization. |
| Approach: | They propose a method that utilizes latent multi-granularity information in annotated bioNER datasets to alleviate the lack of training samples. |
| Outcome: | The proposed model improves over the BioBERT baseline and can get more than 3% improvement of F1score in low-resource scenarios. |
Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems (2026.acl-long)
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| Challenge: | Rapid urbanization and surging vehicle ownership intensify congestion . rapid urbanization drives crash rates, slow emergency response, and burden transit-poor communities . |
| Approach: | They introduce a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC) they use reinforcement learning and network communication to convert LLM into a traffic-control model that operates like a human traffic agent. |
| Outcome: | The proposed model outperforms baselines and training-intensive RL controllers on a simulated traffic environment and reduces queue lengths by more than 5%. |
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (2020.coling-main)
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| Challenge: | Recent studies have shown that inter-sentence information is helpful for improving the performance of document-level Neural Machine Translation models, but what information should be regarded as context remains ambiguous. |
| Approach: | They propose a cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. |
| Outcome: | The proposed model achieves substantial improvements over the state-of-the-art models on NIST evaluation sets. |