Papers by Yong Xie
Agentic Knowledgeable Self-awareness (2025.acl-long)
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Shuofei Qiao, Zhisong Qiu, Baochang Ren, Xiaobin Wang, Xiangyuan Ru, Ningyu Zhang, Xiang Chen, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
| Challenge: | Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks. |
| Approach: | They propose a data-centric approach that applies agents with knowledgeable self-awareness like humans to a heuristic situation judgement criterion to mark special tokens on their self-explored trajectories for collecting training data. |
| Outcome: | The proposed paradigm outperforms baseline models on various tasks with minimal external knowledge. |
Shortcuts Arising from Contrast: Towards Effective and Lightweight Clean-Label Attacks in Prompt-Based Learning (2024.emnlp-main)
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| Challenge: | Prompt-based learning paradigms are vulnerable to backdoor attacks, requiring false activations and false data augmentation. |
| Approach: | They propose a method that uses triggers to create stronger shortcuts by leveraging activation values and data selection strategies to create the shortcuts. |
| Outcome: | The proposed method is based on the concept that a backdoor acts as a shortcut and can achieve high effectiveness and stealthiness at low poisoning rates. |
MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models? (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models lack information asymmetry with real-world situations. |
| Approach: | They propose a benchmark to evaluate the human-like motivational and behavioral reasoning ability of LLMs with detailed, realistic situations. |
| Outcome: | The proposed benchmark compared LLMs with real-world scenarios on seven model families and found that the most advanced models struggle with understanding "love & belonging" needs. |
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)
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| Challenge: | Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points . |
| Approach: | They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points. |
| Outcome: | The proposed model surpasses existing models on ArgKP and QAM datasets. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.emnlp-main)
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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang
| Challenge: | Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs). |
| Approach: | They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding. |
| Outcome: | The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding. |
Automated Fine-Grained Mixture-of-Experts Quantization (2025.findings-acl)
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| Challenge: | specialized quantization framework for Mixture of Experts architectures is inadequate for model compression. |
| Approach: | They propose a specialized quantization framework for Mixture of Experts architectures . they find that expert networks exhibit distinctive channel-wise outlier distributions ." |
| Outcome: | The proposed framework improves on the Mixtral-8x7b-v0.1 architecture while maintaining minimal computational overhead. |
Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)
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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou
| Challenge: | Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments. |
| Approach: | They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts. |
| Outcome: | Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability. |
Learning from Contrastive Prompts: An Automated Prompt Optimization Framework (2026.findings-acl)
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| Challenge: | Existing prompt optimization methods often underperform due to learning exclusively from incorrect samples. |
| Approach: | They propose a framework that leverages contrastive prompts to distinguish between high- and low-performing cases. |
| Outcome: | The proposed framework can generalize across open and proprietary models and NLU benchmarks. |
Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level (2024.findings-naacl)
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| Challenge: | Existing methods for document summarization focus on one type of relation, neglecting the simultaneous effective modeling of both relations. |
| Approach: | They propose a graph neural network-based approach to local and global document summarization using hierarchical discourses. |
| Outcome: | The proposed approach improves on two benchmark datasets and shows that hierarchical structures are important for document summarization. |
Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)
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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li
| Challenge: | Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. |
| Approach: | They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques. |
| Outcome: | The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs. |
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking (2024.eacl-long)
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| Challenge: | Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. |
| Approach: | They propose a framework to integrate Chinese geographic semantics into re-ranking pipelines. |
| Outcome: | The proposed framework improves on two Chinese benchmark datasets. |
Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)
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| Challenge: | Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles. |
| Approach: | They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data . |
| Outcome: | Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases . |
COMBO: A Complete Benchmark for Open KG Canonicalization (2023.eacl-main)
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| Challenge: | Existing datasets for open KG canonicalization only provide gold entity-level canonization for noun phrases. |
| Approach: | They propose a complete benchmark for open KG canonicalization that provides gold ontology-level canonization for relation phrases and source sentences for extraction. |
| Outcome: | The proposed method improves relation canonicalization and ontology-level canonization of the noun phrase. |
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
| Challenge: | Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question . |
| Approach: | They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance . |
| Outcome: | The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered . |
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023.findings-acl)
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| Challenge: | Existing augmentation techniques manipulate words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the text. |
| Approach: | They propose a novel Entity-to-Text based data augmentation technique called EnTDA to add, delete, replace or swap entities in the original text. |
| Outcome: | The proposed technique generates semantically coherent and entity preserving texts on thirteen NER tasks and two settings. |
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025.emnlp-main)
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Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in machine writing such as open domain long-form generation. |
| Approach: | They propose a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection to improve the knowledge density of generated articles. |
| Outcome: | The proposed framework improves the knowledge density of generated articles without compromising metrics such as coherence and depth. |
Knowledge Boundary of Large Language Models: A Survey (2025.acl-long)
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| Challenge: | Large language models (LLMs) store vast amount of knowledge in their parameters, but they still have limitations in the memorization and utilization of certain knowledge. |
| Approach: | They propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. |
| Outcome: | The proposed definition of the LLM knowledge boundary and taxonomy categorizes knowledge into four distinct types . aims to offer a comprehensive overview, facilitate access to key issues, and inspire further advancements in LLM research. |
Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc. |
| Approach: | They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent. |
| Outcome: | The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities. |
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods. |
| Approach: | They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map. |
| Outcome: | The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021. |
Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)
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| Challenge: | Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant. |
| Approach: | They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction. |
| Outcome: | The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset. |
EvolveSearch: An Iterative Self-Evolving Search Agent (2025.emnlp-main)
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Ding-Chu Zhang, Yida Zhao, Jialong Wu, Liwen Zhang, Baixuan Li, Wenbiao Yin, Yong Jiang, Yu-Feng Li, Kewei Tu, Pengjun Xie, Fei Huang
| Challenge: | Existing approaches to enabling LLM web search proficiency struggle with data production in open-search domains, while supervised fine-tuning struggles with data utilization efficiency. |
| Approach: | They propose an iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without external human-annotated reasoning data. |
| Outcome: | EvolveSearch achieves 4.7% improvement over current state-of-the-art in seven benchmarks . supervised fine-tuning struggles with data production in open-search domains compared with RL . |
Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)
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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao
| Challenge: | Existing information-seeking (IS) agents rely on the web for their information acquisition. |
| Approach: | They propose a browser-action framework that decouples interaction control from page exploration through a nested structure. |
| Outcome: | Empirical results show that NestBrowse offers clear benefits in practice. |
WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)
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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. |
| Approach: | They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm. |
| Outcome: | The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios. |
Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference (2025.emnlp-main)
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| Challenge: | Existing methods to detect the knowledge boundary of Vision Large Language Models (VLLMs) are expensive and require indiscriminate retrieval to address questions that require real-time information or are knowledge-intensive. |
| Approach: | They propose a method that fine-tunes a VLLM on an automatically constructed dataset for boundary identification. |
| Outcome: | The proposed method reduces indiscriminate retrieval while maintaining or improving the performance of a VLLM on an automatically constructed dataset. |
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling (2025.findings-naacl)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced their performance in various natural language processing tasks. |
| Approach: | They propose a robust and pluggable knowledge rewriter that is optimized for LLM generation by supporting the model's supportiveness. |
| Outcome: | The proposed model can be used to rewrite knowledge in a supervised manner. |
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction (2022.naacl-main)
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| Challenge: | Existing models are vulnerable to adversarial attacks, but their vulnerability is underexplored. |
| Approach: | They propose to concatenate a perturbed but semantically similar tweet into a model that fools stock prediction models. |
| Outcome: | The proposed method achieves consistent success rates and causes significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet. |
Recall, Expand, and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing (2023.acl-long)
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| Challenge: | State-of-the-art (SOTA) methods use the cross-encoder architecture to concatenate a mention (and its context) with each type and feed it into a pretrained language model (PLM) to score their relevance. |
| Approach: | They propose to perform entity typing in a recall-expand-filter manner and use a novel model to encode and score all these K candidates in one forward pass. |
| Outcome: | The proposed method is thousands of times faster than the CE-based architecture and is very efficient in fine-grained (130 types) and coarse-grain (9 types) entity typing. |
ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions (2025.emnlp-industry)
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| Challenge: | Recent advances in conversational information seeking (CIS) suggest a remedy for the lack of interactive clarification when people face unfamiliar domains. |
| Approach: | They propose a fully autonomous conversational information-seeking agent that couples large language models with a set of domain-specific tools to provide product demand clarification. |
| Outcome: | The proposed agent can iterate over 2,000 automatically generated sessions and score high on real-world evaluations without human annotation. |
Graph Propagation based Data Augmentation for Named Entity Recognition (2023.acl-short)
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| Challenge: | Synthetic data suffer from poor diversity, which leads to performance limitations. |
| Approach: | They propose a graph-propagated data augmentation framework for named entity recognition that uses graph propagation to build relationships between labeled data and unlabeled natural texts. |
| Outcome: | The proposed framework improves on a low-resource named entity recognition dataset. |
WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence. |
| Approach: | They propose a reinforcement learning framework that decouples planning and execution. |
| Outcome: | The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks. |
Retrieved In-Context Principles from Previous Mistakes (2024.emnlp-main)
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| Challenge: | Recent advances in in-context learning (ICL) have limited customization and inadequate error coverage. |
| Approach: | They propose a method to retrieve in-context principles from mistakes to improve model performance. |
| Outcome: | The proposed framework enhances model performance when applied to various prompting strategies. |
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)
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Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang
| Challenge: | Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well. |
| Approach: | They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet. |
| Outcome: | The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future. |
MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task that aims at classifying mention spans into entity types. |
| Approach: | They propose a variational memory-augmented few-shot named entity recognition model that uses a memory module to store information from source domain and retrieve relevant information from the memory to augment few-shot task in target domain. |
| Outcome: | The proposed model can adapt the learned knowledge from source domain to target domain and achieve superior performance on English and Chinese cross domain few-shot NER datasets. |
Do PLMs Know and Understand Ontological Knowledge? (2023.acl-long)
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| Challenge: | Existing studies on pretrained language models focus mainly on factual knowledge, lacking a systematic probing of ontological knowledge. |
| Approach: | They investigate whether Pretrained Language Models store ontological knowledge and have a semantic un- derstanding of the knowledge rather than rote memorization of the surface form. |
| Outcome: | The proposed models can memorize certain ontological knowledge and perform logical reasoning with given knowledge according to ontological entailment rules. |
BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents (2026.findings-acl)
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Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang, Zhongwang Zhang, Xixi Wu, Rui Ye, Zile Qiao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou
| Challenge: | Existing work on confidence in LLMs is limited. |
| Approach: | They propose to use confidence scores to determine model answer quality and encourage model to try again until it reaches satisfactory confidence level. |
| Outcome: | The proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget methods. |
Efficient Continual Pre-training for Building Domain Specific Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are typically trained entirely on domain corpus to excel at handling domain-specific tasks. |
| Approach: | They propose a continual pre-training strategy to build domain-specific LLMs over existing open-domain LLM. |
| Outcome: | The proposed model outperforms existing LLMs with 10% of corpus size and cost without any degradation on open-domain tasks. |
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process (2024.emnlp-main)
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| Challenge: | Existing studies on large-scale labeled support sets are not feasible in practical scenarios. |
| Approach: | They introduce a language model-based determinant point process that considers uncertainty and diversity of unlabeled instances for optimal selection. |
| Outcome: | The proposed method can effectively select canonical examples on 9 NLU and 2 Generation datasets. |
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers (2024.acl-long)
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| Challenge: | Existing transformer-based models struggle with long-sequence processing due to computational costs . a framework to enhance long-content processing of transformers is proposed . |
| Approach: | They propose a framework to enhance long-sequence processing of transformers by three steps . they demonstrate that the framework significantly outperforms prior long-quence processors . |
| Outcome: | The proposed framework outperforms baseline models on long-sequence summarization and reading comprehension tasks. |
Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)
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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution. |
| Approach: | They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research . |
| Outcome: | The proposed model can be used to analyze the evolution of parametric knowledge in LLMs. |
RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)
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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries. |
| Approach: | They propose a framework for training query rewriting models that leverages a reranker framework. |
| Outcome: | The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models. |
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario (2024.findings-emnlp)
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| Challenge: | Existing tool learning methods focus on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness. |
| Approach: | They propose to predict query performance and cost required to accomplish a given task . they then assign queries to the optimal tools in a cost-effective manner . |
| Outcome: | The proposed method achieves higher performance at lower cost compared to baseline approaches. |
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)
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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
| Challenge: | Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks. |
| Approach: | They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment. |
| Outcome: | The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment. |
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field (2022.emnlp-main)
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| Challenge: | Entity typing assigns semantic types to entities mentioned in text. |
| Approach: | They propose to use an undirected graphical model to formulate the UFET problem by combining unary potentials with a pairwise conditional random field model. |
| Outcome: | The proposed model outperforms the existing model with little cost and is thousands of times faster than the existing neural network module. |
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization (2025.findings-naacl)
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| Challenge: | a new approach to timeline summarization is proposed for open-domain news content . large language models (LLMs) can be used to extract and organize news events from multiple documents . |
| Approach: | They propose a method to integrate Large Language Models into news timeline summarization by iterating on how events are linked and posing new questions. |
| Outcome: | The proposed system is able to generate and refresh chronological summaries based on documents retrieved in each round. |
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (2024.findings-acl)
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| Challenge: | Retrieval Augmented Generation can be used to process long contexts in Open-Domain Question-Answering tasks. |
| Approach: | They propose a method to cover longer contexts in Open-Domain Question-Answering tasks by using a small encoder language model and cross-attention with origin inputs. |
| Outcome: | The proposed method can cover longer contexts while keeping the computing requirements close to the baseline. |