Papers by Siqi Liu
ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning. |
| Approach: | They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition . |
| Outcome: | The proposed framework improves performance and basic understanding of large language models. |
EDIS: Entity-Driven Image Search over Multimodal Web Content (2023.emnlp-main)
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| Challenge: | Existing image retrieval methods require large datasets and a large candidate set. |
| Approach: | They propose a news-domain dataset for cross-modal image search with 1 million web images . they propose combining multimodal image-text pairs with a million candidates . |
| Outcome: | The proposed dataset challenges state-of-the-art methods with dense entities and the large-scale candidate set. |
AWARE: Agentic Knowledge Warehousing for Contextual Intelligence (2026.findings-acl)
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| Challenge: | Large language models excel in information seeking tasks, but their knowledge is limited in coverage and timeliness. |
| Approach: | They propose an agentic knowledge warehousing framework that transforms unstructured data into minimal, task-conditioned knowledge representations consumable by LLMs. |
| Outcome: | Experiments on GAIA, WebWalker, and BrowseComp-Plus show improvements over baselines. |
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval (2021.naacl-main)
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| Challenge: | Existing pre-trained models suffer from slow inference speed due to cross-modal attention in transformer architecture. |
| Approach: | They propose a multimodal approach that accelerates the inference time of ITR by thousands of times . they extract pre-cached feature indexes offline and employ instant dot-product matching online . |
| Outcome: | The proposed approach outperforms existing models that consume 1000 times magnitude of computational hours using the same features. |
Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.findings-acl)
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| Challenge: | Existing models for encoding long sequences in deep learning suffer from high latency and memory demands. |
| Approach: | They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences. |
| Outcome: | The proposed framework achieves state-of-the-art on several major QA benchmarks. |
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)
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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang
| Challenge: | Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations. |
| Approach: | They propose a benchmark to assess lifelong learning in large language models . they use two episodic datasets rich in narrative structure and character interactions . |
| Outcome: | Experiments on LLMs show that non-parametric methods outperform parametric ones in managing stateful learning. |
FACTS: Table Summarization via Offline Template Generation with Agentic Workflows (2026.findings-acl)
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| Challenge: | Existing methods for query-focused table summarization struggle with complex reasoning and token-limit issues. |
| Approach: | They propose a Fast, Accurate, and Privacy-Compliant table summarization approach via Offline Template Generation. |
| Outcome: | The proposed method outperforms baseline methods on widely-used benchmarks. |
Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach (2024.emnlp-main)
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| Challenge: | Incorrect translation of rare words can severely degrade the accuracy of ST models . |
| Approach: | They propose a retrieval-and-demonstration approach to enhance rare word translation accuracy in ST models by incorporating retrieved examples into ST models. |
| Outcome: | The proposed approach outperforms other modalities and exhibits higher robustness to unseen speakers. |
Contrastive Distillation on Intermediate Representations for Language Model Compression (2020.emnlp-main)
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| Challenge: | Existing methods to compress language models use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. |
| Approach: | They propose a method that uses knowledge distillation to distill knowledge through intermediate layers of the teacher via a contrastive objective. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6% accuracy improvement on HotpotQA. |
| Approach: | They propose a Lexical Diversity-aware RAG method to address the biases in relevant information retrieval and utilization induced by lexical diversity. |
| Outcome: | Extensive experiments on widely used benchmarks show the proposed method yields a 10.6% accuracy improvement on HotpotQA. |
The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models (2024.emnlp-main)
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| Challenge: | Using the World Value Survey, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population. |
| Approach: | They use data from the World Value Survey to examine the alignment of LLM values with specific age groups. |
| Outcome: | The proposed model can be used to predict the value of a large language model and to assess its performance on 13 categories. |
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)
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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang
| Challenge: | Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge . |
| Approach: | They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM. |
| Outcome: | The proposed model can model human conversation behaviors with low latency and natural interactions with low delay. |
Exploiting Contextual Knowledge in LLMs through 𝒱-usable Information based Layer Enhancement (2025.acl-long)
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| Challenge: | Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states. |
| Approach: | They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis. |
| Outcome: | The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge. |
Cross-Thought for Sentence Encoder Pre-training (2020.emnlp-main)
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| Challenge: | Existing models to pretrain sentence encoders with large unlabeled corpus are lacking in linguistic information retrieval. |
| Approach: | They propose a novel approach to pre-training sequence encoder using transformers . they propose to train a Transformer-based sequence encoded over a large set of short sequences based on a set of masked words . |
| Outcome: | The proposed approach outperforms state-of-the-art encoders on hotpotQA by improving intermediate information retrieval performance. |
Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation (2026.findings-acl)
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| Challenge: | Existing methods for training large language models rely heavily on high-quality parallel data, which are often scarce or unavailable for low-resource languages. |
| Approach: | They propose a reinforcement training method using only monolingual text to elevate LLMs’ translation capabilities on massive low-resource languages while retaining their performance on high-resourced languages. |
| Outcome: | The proposed model outperforms LLaMAX, one of the strongest open-source multilingual LLMs on 1,414 language directions on Flores-101 dataset. |
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)
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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang
| Challenge: | Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning. |
| Approach: | They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. |
| Outcome: | Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks. |
Reinforcing Agentic Search Via Reward Density Optimization (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search, but its performance is often hindered by reward sparsity . |
| Approach: | They propose a new research problem to improve the reward obtained per unit of exploration cost by using a system that decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals. |
| Outcome: | The proposed framework outperforms strong baselines on several agentic search benchmarks and achieves comparable performance to that of advanced proprietary models. |
Hierarchical Graph Network for Multi-hop Question Answering (2020.emnlp-main)
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| Challenge: | Existing multi-hop question answering models focus on multi-level reasoning across multiple documents or paragraphs. |
| Approach: | They propose a hierarchical graph network that aggregates clues from scattered texts . they use a set of contextual encoders to initialize nodes on different levels of granularity . |
| Outcome: | The proposed model outperforms existing multi-hop QA approaches on the HotpotQA benchmark. |
Patient Knowledge Distillation for BERT Model Compression (D19-1)
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| Challenge: | Pre-trained language models such as BERT have proven to be highly effective for natural language processing tasks, but the high demand for computing resources hinders their application in practice. |
| Approach: | They propose to compress an original large model (teacher) into an equally-effective lightweight shallow network (student) Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy. |
| Outcome: | The proposed model reduces the computational cost of training models using the teacher model into a lightweight shallow network. |
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 (2026.acl-long)
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Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang
| Challenge: | Existing approaches to solving mathematical problems fall into two broad categories: informal methods and formal methods. |
| Approach: | They propose to use LLM natural-language reasoning to discover answers . they introduce Discover And Prove framework that rewrites Hard Mode statements into Easy Mode ones for existing ATP provers. |
| Outcome: | The proposed framework can be used to prove hard mode statements on ATP benchmarks. |
DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect (2023.findings-acl)
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| Challenge: | Existing methods for binaural audio synthesis are limited in phase estimation, which is crucial for spatial hearing. |
| Approach: | They propose a method to explicitly address the Doppler effect of the moving speaker . it calculates the radial relative velocity of the speaker in spherical coordinates . |
| Outcome: | The proposed method improves the representative WarpNet and BinauralGrad backbones in phase error metric and reaches a new state of the art (SOTA) it is compared with the current method which is limited in phase estimation . |
Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking (2024.findings-acl)
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Zefeng Zhang, Jiawei Sheng, Zhang Chuang, Liangyunzhi Liangyunzhi, Wenyuan Zhang, Siqi Wang, Tingwen Liu
| Challenge: | Existing methods to link ambiguous mentions to entities in multimodal knowledge graphs rely on partial correlations. |
| Approach: | They propose a framework that leverages multi-element correlations to bridge modality gap and enable fine-grained semantic matching by exploiting correlation between multimodal features and entities. |
| Outcome: | The proposed framework outperforms state-of-the-art models and confirms the effectiveness of the proposed method. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data (2022.acl-long)
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| Challenge: | Experimental results show that REtrieving from the traINing datA only can lead to significant gains on multiple NLG and NLU tasks. |
| Approach: | They propose to retrieve training instances from traINing datA and concatenate them with input to generate output. |
| Outcome: | The proposed method achieves state-of-the-art results on XSum, BigPatent, and CommonsenseQA. |
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2021.findings-acl)
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| Challenge: | PLATO-2 is a high-quality open-domain chatbot that can generate one-to-many mappings and improve response quality. |
| Approach: | They propose a curriculum learning process to build a high-quality open-domain chatbot . they use a coarse-grained generation model and latent variables to train a generative model . |
| Outcome: | The proposed model improves on Chinese and English data and can generate diverse responses and select the best response. |
Structured Optimal Brain Pruning for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing pruning methods for Large Language Models rely on unstructured pruning or require special hardware to accelerate computation. |
| Approach: | They propose a retraining-free structured pruning method called SoBP . they evaluate the effectiveness of SoBP across 14 models from 3 LLM families . |
| Outcome: | The proposed method outperforms current state-of-the-art pruning methods on 8 datasets. |