Papers by Yi Han
Dictionary Guided Sparse Logit Editing for Reliable Jailbreak Attacks (2026.findings-acl)
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| Challenge: | Existing methods to optimize large language models suffer from high computational costs and produce uninterpretable, high-perplexity inputs. |
| Approach: | They propose a sparse index-based intervention that bypasses guardrails via sparser logit editing. |
| Outcome: | The proposed method bypasses guardrails by modifying pre-softmax logits without gradients or auxiliary models. |
Leveraging LLM-Generated Schema Descriptions for Unanswerable Question Detection in Clinical Data (2025.coling-main)
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| Challenge: | Existing methods rely on model uncertainty but lack interpretability and data imbalance. |
| Approach: | They propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. |
| Outcome: | The proposed model improves interpretability and improves accuracy in binary classification tasks. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. |
| Approach: | They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. |
| Outcome: | The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge. |
Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)
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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available. |
| Approach: | They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs. |
| Outcome: | The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer. |
Adapting General-Purpose Embedding Models to Private Datasets Using Keyword-based Retrieval (2025.findings-acl)
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| Challenge: | Text embedding models show strong performance on generic benchmarks, but their effectiveness diminishes when applied to private datasets. |
| Approach: | They propose a method for adapting general-purpose text embedding models to private datasets . they construct supervisory signals from the ranking of keyword-based retrieval results . |
| Outcome: | The proposed method improves retrieval performance across domains, datasets, and models. |
DeepMaven: Deep Question Answering on Long-Distance Movie/TV Show Videos with Multimedia Knowledge Extraction and Synthesis (2023.eacl-main)
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| Challenge: | Long video content understanding poses a challenging set of research questions as it involves long-distance, cross-media reasoning and knowledge awareness. |
| Approach: | They propose a framework which extracts events, entities, and relations from the rich multimedia content in long videos to pre-construct movie knowledge graphs. |
| Outcome: | The proposed framework performs competitively for both the new DeepMovieQA and the pre-existing MovieQA dataset. |
Language Model is Suitable for Correction of Handwritten Mathematical Expressions Recognition (2023.emnlp-main)
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| Challenge: | Existing approaches to handwritten mathematical expression recognition are limited by CFGs and pre-generated triplet data. |
| Approach: | They propose an architecture that integrates recognition and language features to output corrected sequences while optimizing with a string decoder recognition model. |
| Outcome: | The proposed architecture outperforms state-of-the-art methods on CROHME datasets. |
CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent Layers (2024.acl-long)
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| Challenge: | Existing methods to improve inference efficiency target to reduce per-layer latency, but ignore cumulative latency due to number of layers. |
| Approach: | They propose to identify quasi-independent layers that can be concurrently computed to significantly decrease inference latency. |
| Outcome: | Empirical results show that the proposed method reduces latency by 48.3% on LLaMA-33B while maintaining close level of performance. |
Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)
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Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun
| Challenge: | Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases. |
| Approach: | They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram. |
| Outcome: | The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries. |
Distilling LLM Reasoning into Dense Encoders: Bridging the Accuracy-Efficiency Gap in Recommendation (2026.findings-acl)
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| Challenge: | Existing distillation approaches target Small Language Models (SLMs) or Conventional Recommendation Models, but face a critical trade-off between computational cost and semantic reasoning capacity. |
| Approach: | They propose a framework that establishes a text encoder as the optimal student architecture for scalable recommendation. |
| Outcome: | Experiments on four datasets show that the proposed framework outperforms state-of-the-art models and achieves significantly reduced latency. |
Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents. |
| Approach: | They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process. |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
Automating Interlingual Homograph Recognition with Parallel Sentences (2022.findings-aacl)
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| Challenge: | Existing methods for interlingual homograph recognition require linguistic knowledge and massive annotation work. |
| Approach: | They propose an automatic interlingual homograph recognition method based on cross-lingual word embedding similarity and co-occurrence of form-identical words in parallel sentences. |
| Outcome: | The proposed method can make accurate predictions across languages. |
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors (2024.lrec-main)
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| Challenge: | Existing methods to learn new relations with limited labeled data are prone to catastrophic forgetting and overfitting. |
| Approach: | They propose a framework that uses prompts to acquire more generalized knowledge . they propose CFRE to continuously learn new relations while retaining knowledge of old ones . |
| Outcome: | The proposed method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios. |
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play (2024.emnlp-demo)
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Sha Li, Revanth Gangi Reddy, Khanh Nguyen, Qingyun Wang, Yi Fung, Chi Han, Jiawei Han, Kartik Natarajan, Clare Voss, Heng Ji
| Challenge: | Complex news events require swift responses from government and society, authors say . relying on historical events to project the future is insufficient, they say - a simulator for complex news events is needed . |
| Approach: | They propose a controllable complex news event simulator guided by event schema and user-provided assumptions . they incorporate a geo-diverse commonsense and cultural norm-aware knowledge enhancement component . |
| Outcome: | The proposed simulator achieves higher coherence and appropriateness than existing models. |
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang
| Challenge: | Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG. |
| Approach: | They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern. |
| Outcome: | The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen
| Challenge: | Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models. |
| Approach: | They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model. |
| Outcome: | The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026.findings-acl)
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| Challenge: | State-of-the-art large language models (LLMs) are vulnerable to jailbreak attacks, such as GCG and AutoDAN. |
| Approach: | They propose to take the advances of online In-Context Learning and an offline defensive suffix and optimize them using an iterative algorithm and an online stochastic random search to identify the most effective ICL demonstrations. |
| Outcome: | The proposed method reduces attack success rate to nearly *0% while maintaining the model’s utility on benign tasks and incurring only *negligible* computational overhead. |
ToViLaG: Your Visual-Language Generative Model is Also An Evildoer (2023.emnlp-main)
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| Challenge: | Recent large-scale Visual-Language Generative Models (VLGMs) generate toxic content, e.g., offensive text and pornography images, raising significant ethical risks. |
| Approach: | They propose a bottleneck-based detoxification method to reduce toxicity while maintaining comparable generation quality. |
| Outcome: | The proposed method could reduce toxicity while maintaining comparable generation quality. |
Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation . fixed anchors can enforce constraints, but they often impose rigid spans, leading to truncated reasoning . |
| Approach: | They propose a method that dynamically estimates end-anchor positions to adjust generation length before iterative infilling. |
| Outcome: | The proposed method improves format compliance and answer accuracy on GSM8K and MATH. |
Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)
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| Challenge: | Existing MWP solvers do not handle variants that can be derived via mathematical manipulation. |
| Approach: | They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description. |
| Outcome: | The proposed solver is able to decode multiple expression variants and correct them . it is based on a unified tree structure and is available on Math23K and MAWPS. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Defining a New NLP Playground (2023.findings-emnlp)
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Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
Definition Generation for Automatically Induced Semantic Frame (2024.findings-acl)
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| Challenge: | Semantic frames are conceptual structures that describe specific types of situations or events. |
| Approach: | They propose to generate frame definitions from a set of frame-evoking words using a large language model. |
| Outcome: | The proposed task incorporates frame element reasoning as chain-of-thought to enhance the inclusion of correct frame elements in the generated definitions. |
Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction (2024.findings-emnlp)
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| Challenge: | Prior work in ABSA has investigated opinion extraction as an important subtask, but these works only label concise, *explicitly*-stated opinion spans. |
| Approach: | They propose a new ABSA dataset with implicit opinion span annotations . they use paragraph-length inputs and prompted-LLM baselines to evaluate the dataset . |
| Outcome: | The proposed dataset presents significant challenges for fully-supervised models and LLMs. |
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to find relational facts from texts lack hierarchical information of relations. |
| Approach: | They propose a hierarchical classification framework which extracts relation in a top-down manner. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora . |
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)
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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao
| Challenge: | Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis. |
| Approach: | They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge. |
| Outcome: | The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks. |
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)
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| Challenge: | Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. |
| Approach: | They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. |
| Outcome: | The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%. |
MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)
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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang
| Challenge: | Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. |
| Approach: | They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. |
| Outcome: | Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. |
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation (2025.naacl-long)
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Mahnaz Koupaee, Jake W. Vincent, Saab Mansour, Igor Shalyminov, Han He, Hwanjun Song, Raphael Shu, Jianfeng He, Yi Nian, Amy Wing-mei Wong, Kyu J. Han, Hang Su
| Challenge: | Existing approaches to evaluate faithfulness of summaries are often fooled by the fluency of the text and struggle with identifying errors. |
| Approach: | They propose an approach to summary faithfulness evaluation where multiple LLM-based agents are assigned initial stances and forced to come up with a reason to justify belief. |
| Outcome: | The proposed approach can identify ambiguities and have even stronger performance on non-ambiguous summaries. |
Learning Joint Structural and Temporal Contextualized Knowledge Embeddings for Temporal Knowledge Graph Completion (2023.findings-acl)
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| Challenge: | Existing methods that incorporate time information into static knowledge graph embedding ignore the contextual nature of the TKG structure. |
| Approach: | They propose a method that employs pre-trained language models to learn joint Structural and Temporal Contextualized Knowledge Embeddings. |
| Outcome: | The proposed method is superior to existing methods that ignore the contextual nature of the TKG structure. |
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)
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| Challenge: | Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations. |
| Approach: | They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. |
| Outcome: | The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations. |
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models (2025.acl-industry)
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| Challenge: | Tabular data analysis is crucial in many scenarios, yet its complexity and density can make it challenging to determine the most appropriate analysis operations for a new table. |
| Approach: | They propose a tabular data analysis framework that recommends query-code-result triplets for new tables . they propose Rec-Align, a method to further improve recommendation quality . |
| Outcome: | The proposed framework achieves 77.0% top-5 recommendation recall on a dataset designed for tabular data analysis recommendation. |
An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)
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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu
| Challenge: | Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints. |
| Approach: | They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking . |
| Outcome: | The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints. |
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)
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| Challenge: | Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters. |
| Approach: | They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace. |
| Outcome: | The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance. |
Nullspace Disentanglement for Red Teaming Language Models (2025.emnlp-main)
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| Challenge: | Existing work mainly leverages crowd workers to construct test cases. |
| Approach: | They propose a black-box approach that exploits the unique properties of the nullspace to disentangle and regulate the crucial success information within test cases. |
| Outcome: | The proposed approach outperforms baseline methods regarding the attack success rate and excels in aspects of diversity and fluency. |
Feasible is Not Enough: Cost-Aware Optimal Tool-Chain Planning on Multi-Solution Tool Graphs (2026.findings-acl)
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| Challenge: | Existing tools and benchmarks often form tool learning (TL) as a single-solution setting . exploring large-scale TG is computationally expensive, especially under constrained context budgets. |
| Approach: | They propose a framework for learning optimal TL policies over large tool graphs . they train a reinforcement learning agent to acquire transferable expansion skills . |
| Outcome: | The proposed framework improves task success and solution optimality by 46.21% and 66.34% on multiSoTLBench. |
Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)
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| Challenge: | Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. |
| Approach: | They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern. |
| Outcome: | The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models. |
DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.acl-long)
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| Challenge: | Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity. |
| Approach: | They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation. |
| Outcome: | The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. |
Generative Personality Simulation via Theory-Informed Structured Interview (2026.eacl-long)
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Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao, Frederick L. Oswald
| Challenge: | Personality structured interviews are often lacking in advancing social science research. |
| Approach: | They propose a method to incorporate psychological insights into LLM simulations . they use a measure theory grounded evaluation procedure to evaluate reliability and validity . |
| Outcome: | The proposed method improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes. |
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration (2025.findings-emnlp)
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| Challenge: | Query-to-Recommendation framework integrates large langucage models into recommendation systems . but it faces training-induced bias and bottlenecks from serialized architecture . |
| Approach: | They propose a parallel recommendation framework that decouples LLMs from candidate pre-selection and direct retrieval over the entire item pool. |
| Outcome: | The proposed framework decouples LLMs from candidate pre-selection and enables direct retrieval over the entire item pool. |
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering (2026.findings-acl)
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FU Yuqing, Yimin Deng, Wanyu Wang, Yuhao Wang, Yejing Wang, Hongshi Liu, Yiqi Wang, Xiao Han, Maolin Wang, Guoshuai Zhao, Yi Chang, Xiangyu Zhao
| Challenge: | Existing approaches to multi-hop question answering lack effective control over reasoning paths, leading to astray results. |
| Approach: | They propose a framework for multi-hop question answering that trains an end-to-end reasoning path navigator to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model. |
| Outcome: | The proposed framework trains an end-to-end reasoning path navigator . it is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model . |
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning. |
| Approach: | They propose a framework that enables LLMs to create their own tools using documentation and code realization. |
| Outcome: | The proposed framework outperforms existing chain-of-thought, program-of thought, and tool-using baselines on MATH and TabMWP benchmarks. |