Papers by Linqi Song
LoRAN: Improved Low-Rank Adaptation by a Non-Linear Transformation (2024.findings-emnlp)
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| Challenge: | Recent methods for fine-tuning large language models have shown great improvements on a wide range of NLP tasks. |
| Approach: | They propose to introduce a non-linear transformation to improve performance of adapters by introducing a low-rank adaptation to fit the accumulated weight updates. |
| Outcome: | The proposed method outperforms a baseline on SAMSum and 20 Newsgroups tasks and even improves the classification task by 1.95 points when a lower rank is applied. |
What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in reasoning with large language models have popularized Long Chain-of-Thought (LCoT) a framework that converts sequential LCoTs into hierarchical tree structures enables deeper structural analysis of LLM reasoning. |
| Approach: | They propose a framework that converts sequential LCoTs into hierarchical tree structures and enables deeper structural analysis of LLM reasoning. |
| Outcome: | The proposed framework can be used to analyze LLM reasoning in a variety of tasks and models. |
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
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Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter (2020.emnlp-main)
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| Challenge: | Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. |
| Approach: | They propose to use semantic role labeling to provide additional guidance for multi-turn dialogue rewriting models. |
| Outcome: | The proposed model outperforms existing models on multi-turn dialogue rewriting tasks. |
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)
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| Challenge: | Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information. |
| Approach: | They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary. |
| Outcome: | The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets. |
Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining (2024.findings-acl)
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| Challenge: | Existing unidirectional chaining methods suffer from low prediction accuracy and efficiency. |
| Approach: | They propose a bidirectional chaining method which dynamically switches to depth-first reasoning in the opposite reasoning direction when it encounters multiple branching options within the current direction. |
| Outcome: | The proposed method achieves sizable accuracy boots over unidirectional chaining frameworks on four challenging logical reasoning datasets. |
Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing (2024.emnlp-main)
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| Challenge: | Existing studies have focused on using Large Language Models to improve translation quality . language mismatch and repetition are two of the main problems with LLMs . |
| Approach: | They propose to leverage model editing methods to reduce language mismatch and repetition . they propose to fetch intersections of locating results under different language settings . |
| Outcome: | The proposed methods reduce language mismatch and repetition ratios and enhance translation quality in most cases. |
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling (2021.emnlp-main)
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| Challenge: | Existing CSRL parsers struggle to handle conversational structural information. |
| Approach: | They propose a conversational semantic role labeling task which explicitly encodes speaker dependent information and proposes a multi-task learning method to further improve the model. |
| Outcome: | The proposed model outperforms baselines on benchmark datasets on conversation-based tasks. |
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL (2026.findings-acl)
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| Challenge: | Existing NL2SQL systems rely on in-context learning with only correct examples . current test-time scaling methods often decompose questions arbitrarily, resulting in poor performance . |
| Approach: | They propose a structured decomposition and experience-aware self-correction framework for NL2SQL . they build a dynamic memory of successful queries and historical error–fix pairs . |
| Outcome: | The proposed framework achieves 68.5% execution accuracy on BIRD, setting new state of the art among open, zero-fine-tuning methods. |
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings (2022.findings-acl)
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| Challenge: | Existing approaches to contrastive learning are heavily affected by superficial features like sentence length and syntax. |
| Approach: | They propose a semantic-aware contrastive learning framework for sentence embeddings that explores the pseudo-token space representation of a sentence while eliminating the impact of superficial features such as sentence length and syntax. |
| Outcome: | The proposed framework outperforms the state-of-the-art on six standard semantic textual similarity tasks while maintaining an additional queue to store the representation of sentence embeddings. |
Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)
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| Challenge: | Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. |
| Approach: | They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives. |
| Outcome: | The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora . |
Understanding and Patching Compositional Reasoning in LLMs (2024.findings-acl)
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| Challenge: | LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. |
| Approach: | They propose a lightweight method to patch compositional reasoning errors via editing the located MHSA modules in LLMs. |
| Outcome: | The proposed method can be used to patch compositional reasoning errors using MHSA modules located within the layers of the LLMs. |
ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)
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Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song
| Challenge: | Existing evaluation methods for large language models are labor-intensive and lack efficiency. |
| Approach: | They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background. |
| Outcome: | The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics. |
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation (2024.acl-long)
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| Challenge: | Existing MCTG methods face a noticeable performance drop in compositional testing. |
| Approach: | They propose a benchmark to evaluate compositional generalization of MCTG methods by combining multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol. |
| Outcome: | The proposed framework improves compositional generalization performance by 3.64% and 94.4% in compositional testing. |
VCSUM: A Versatile Chinese Meeting Summarization Dataset (2023.findings-acl)
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| Challenge: | Compared to news and chat summarization, meeting summarizing is decelerated by the limited data. |
| Approach: | They propose a Chinese meeting summarization dataset that provides annotations for each transcript and a set of benchmark models to facilitate further research. |
| Outcome: | The proposed model can be used to summarize the content of meeting transcripts in Chinese. |
Domain-Adaptive Pretraining Methods for Dialogue Understanding (2021.acl-short)
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| Challenge: | Recent advances in pretraining methods have achieved promising results on NLP tasks . however, it is unclear which pretraining objective is the most effective for each downstream task . |
| Approach: | They evaluate the effectiveness of domain-adaptive pretraining objectives on downstream tasks . they use open-domain data to pretrain language models like BERT and SpanBERT . |
| Outcome: | The proposed model improves on two dialogue understanding tasks with domain-adaptive pretraining objectives. |
Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models (2025.findings-acl)
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| Challenge: | Existing task vector-based model merging methods apply uniform coefficients across all parameters, overlooking varying parameter importance both within and across tasks. |
| Approach: | They propose a sensitivity-guided coefficient adjustment method that optimizes existing model merging techniques by operating at both task-specific and cross-task levels. |
| Outcome: | The proposed method outperforms existing model merging techniques on mistral 7B and LLaMA2 7B/13B models and enables them to outperformed specialized models. |
Fine-grained Conversational Decoding via Isotropic and Proximal Search (2023.emnlp-main)
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| Challenge: | Existing text decoding methods are not tailoring for dialogue generation. |
| Approach: | They propose a fine-grained conversational decoding method that generates a semantic-concentrated response while maintaining informativeness and discrimination against the context. |
| Outcome: | The proposed method outperforms existing decoding strategies in the dialogue field across both automatic and human evaluation metrics. |
On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation (2026.findings-acl)
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| Challenge: | Recent studies have focused on the non-deterministic properties of language models, but these properties remain under-explored in machine translation. |
| Approach: | They propose a method that evaluates MT systems and identifies temperature-constrained non-deterministic MT as a distinct phenomenon. |
| Outcome: | The proposed framework provides higher-quality candidates than Deterministic MT under temperature constraints. |