Papers by Xifeng Yan
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
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| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
Language Model Detoxification in Dialogue with Contextualized Stance Control (2022.findings-emnlp)
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| Challenge: | Existing work on Language Model detoxification has focused on reducing the toxicity of the generation itself without consideration of the context. |
| Approach: | They propose a method to do context-dependent detoxification without taking into account the stance of the generated response. |
| Outcome: | The proposed method can learn the context-dependent stance control strategies while keeping a low self-toxicity of the underlying LM. |
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling (2024.acl-long)
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Zekun Li, Zhiyu Chen, Mike Ross, Patrick Huber, Seungwhan Moon, Zhaojiang Lin, Xin Dong, Adithya Sagar, Xifeng Yan, Paul Crook
| Challenge: | Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. |
| Approach: | They propose a method for solving dialogue state tracking (DST) with large language models through function calling. |
| Outcome: | The proposed approach improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data-to-text generation rely on labeled data, which is costly to acquire and limits their application to new tasks and domains. |
| Approach: | They propose to leverage pre-training and transfer learning to address this problem by leveraging a general knowledge-grounded generation model and a knowledge-based model. |
| Outcome: | The proposed model can generate knowledge-enriched text on a knowledge-grounded text corpus crawled from the web in three settings. |
Limitations of Language Models in Arithmetic and Symbolic Induction (2023.acl-long)
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| Challenge: | Recent work has shown that large pretrained Language Models (LMs) can perform remarkably well on a range of NLP tasks but they have limitations on basic symbolic manipulation tasks such as copy, reverse, and addition. |
| Approach: | They propose to use explicit positional markers, fine-grained computation steps, and LMs with callable programs to teach large pretrained Language Models. |
| Outcome: | The proposed model can perform 100% accuracy in OOD and repeating symbols. |
What It Takes to Achieve 100% Condition Accuracy on WikiSQL (D18-1)
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| Challenge: | despite of its simplicity, none of the publicly reported structured query generation models can achieve an accuracy beyond 62%, which is far from enough for practical use. |
| Approach: | They propose a model that can achieve 88.6% condition accuracy on WikiSQL . they ask: why is the accuracy still low for such simple queries? |
| Outcome: | The proposed solution can reach up to 88.6% condition accuracy on the WikiSQL dataset. |
Learning to Compare Financial Reports for Financial Forecasting (2024.findings-eacl)
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| Challenge: | Public companies in the US are required to publish annual reports that contain over 25,000 words across all sections and a high percentage of boilerplate content that does not change much year-to-year. |
| Approach: | They propose to model complex, cross-document relationships between financial reports using paired financial reports. |
| Outcome: | The proposed model can predict company risk and correlation from financial reports . the proposed model is able to recognize complex, nuanced relationships with complex signals . |
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention (P19-1)
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| Challenge: | Existing models for limited-domain RNNs are difficult to scale due to the complexity of the inputs. |
| Approach: | They propose to use dialog acts to build a multi-layer hierarchical graph with a disentangled self-attention network. |
| Outcome: | The proposed model improves on the baselines on automatic and human evaluation metrics. |
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Task-adaptive pre-training (TAPT) and Self-training can be complementary with simple TFS protocol. |
| Approach: | They propose to use task-adaptive pre-training and self-training to combine TAPT and ST with a simple TFS protocol to achieve strong combined gains across six datasets. |
| Outcome: | The proposed method can achieve strong combined gains across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. |
Train a Unified Multimodal Data Quality Classifier with Synthetic Data (2025.findings-emnlp)
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Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard, Ritesh Sarkhel, Sanket Lokegaonkar, Jingbo Shang, Xifeng Yan, Nasser Zalmout, Xian Li
| Challenge: | Multimodal Large Language Models are pre-trained on image-text caption data and interleaved document data. |
| Approach: | They propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to filter image-text caption and interleaved data. |
| Outcome: | The proposed method enables efficient creation of sample-score pairs for caption and interleaved data to train UniFilter. |
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, making them increasingly integral to various applications. |
| Approach: | They establish a benchmark to evaluate the robustness of instruction-following LLMs against prompt injection attacks, assessing their ability to discern which instructions to follow and which to disregard. |
| Outcome: | The proposed model is overly sensitive to prompt injection attacks, focusing on the latter part of the prompt without fully understanding the context. |
DiffSkip: Differential Layer Skipping in Large Language Models (2025.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) enforce uniform computation across all tokens. |
| Approach: | They propose to dynamically skip FFN blocks based on self-attention difference . they propose to use a lightweight router module to do the same . |
| Outcome: | The proposed model can skip FFN blocks even in token generation tasks. |
DialSQL: Dialogue Based Structured Query Generation (P18-1)
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| Challenge: | Recent advances in deep learning and semantic parsing have improved the translation accuracy of natural language questions to structured queries. |
| Approach: | They propose a dialogue-based structured query generation framework that leverages human intelligence to boost performance of existing algorithms via user interaction. |
| Outcome: | The proposed framework improves on a WikiSQL dataset from 61.3% to 69.0% using only 2.4 validation questions per dialogue. |
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection (N19-1)
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| Challenge: | Using a pre-defined vocabulary is a common approach to selecting text inputs . however, using a large vocabulary is not economical, as it limits the model's applicability on computation-or memoryconstrained scenarios. |
| Approach: | They propose a more sophisticated variational vocabulary dropout to perform vocabulary selection . they propose two new metrics to measure area under accuracy-vocab curve and Vocab Size under X% accuracy drop . |
| Outcome: | The proposed framework outperforms the baselines on the vocabulary selection problem on multiple NLP classification tasks. |
Global Textual Relation Embedding for Relational Understanding (P19-1)
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| Challenge: | Existing methods to learn textual relation embeddings are lacking in large open-domain corpora. |
| Approach: | They propose to learn a general-purpose embedding of textual relations using a large dataset from Freebase. |
| Outcome: | The proposed embedding can facilitate downstream tasks requiring relational understanding of the text. |
Forecasting Earnings Surprises from Conference Call Transcripts (2023.findings-acl)
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| Challenge: | Earnings conference calls contain over 5,000 words of text and large amounts of industry jargon . this length and domain-specific language present problems for generic pretrained language models. |
| Approach: | They propose a task of predicting earnings surprises from earnings call transcripts and propose linguistic models that use a long document dataset to test financial understanding. |
| Outcome: | The proposed model can predict earnings surprises from earnings conference calls with reasonable accuracy and shows that it is possible to interpret the data with different interpretability methods. |
Controllable Dialogue Simulation with In-context Learning (2022.findings-emnlp)
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| Challenge: | Existing methods to generate annotated dialogues require crowdsourcing, which is expensive and time-consuming. |
| Approach: | They propose a dialogue simulation method based on large language model in-context learning that generates new dialogues and annotations in a controllable way. |
| Outcome: | The proposed method can expand a small set of dialogue data with minimum or zero human involvement and parameter update. |
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)
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Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang
| Challenge: | BrainLoc is a lightweight object detection model guided by fMRI signals. |
| Approach: | They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction . |
| Outcome: | The proposed model improves fMRI-based object detection accuracy and convenience. |
Lifelong Learning of Hate Speech Classification on Social Media (2021.naacl-main)
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| Challenge: | Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. |
| Approach: | They propose to use Variational Representation Learning and a load-balancing self-organizing inductive neural network to learn hate speech classification on social media. |
| Outcome: | The proposed model improves on the lifelong learning techniques on social media. |
Creative and Context-Aware Translation of East Asian Idioms with GPT-4 (2024.findings-emnlp)
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| Challenge: | figurative language is a challenge for human translators, who often choose a context-aware translation . a set of commonly used idioms condenses its figurativ meaning into a few characters . |
| Approach: | They evaluate whether GPT-4 can generate high-quality translations using Pareto-optimal prompting strategies that outperform translation engines from Google and DeepL. |
| Outcome: | The proposed translations outperform translation engines from Google and DeepL at low cost. |
Global Relation Embedding for Relation Extraction (N18-1)
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| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
| Approach: | They propose to embed relations with global statistics of relations to combat the wrong labeling problem of distant supervision. |
| Outcome: | The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models. |
Financial Forecasting from Textual and Tabular Time Series (2024.findings-emnlp)
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| Challenge: | Existing models that combine multiple data sources and combine them to form accurate financial predictions are challenging to model without inductive biases. |
| Approach: | They propose to use numerical financial results, macroeconomic states, and long financial documents to model company earnings relative to analyst expectations. |
| Outcome: | The proposed model outperforms existing models in a simulated trading environment and demonstrates that each modality contains unique information. |
Variational Knowledge Graph Reasoning (N18-1)
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| Challenge: | Existing knowledge graphs have large amount of missing links, which limits their application . a recent study has proposed to design an automated inference model to complete the missing links in large knowledge graph. |
| Approach: | They propose to use variation inference to solve missing links in knowledge graphs . they use a posterior approximator, prior (path finder) and likelihood (path reasoner) |
| Outcome: | The proposed model achieves state-of-the-art on multiple datasets and is highly accurate. |