Papers by Xinran Zhao
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers (2025.emnlp-main)
Copied to clipboard
| Challenge: | Different retrievers offer distinct, often complementary signals, but they are not optimal for all queries. |
| Approach: | They propose a zero-shot, weighted combination of heterogeneous retrievers . they validate this intuition by incorporating specialized non-oracle human information sources . |
| Outcome: | Experiments show that a mixture of heterogeneous retrievers outperforms each retriever and larger models by +10.8% and +3.9% on average. |
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on the confidence calibration of LLMs have not explored the effects of different prompting strategies on LLM performance. |
| Approach: | They propose Fact-and-Reflection prompting which improves LLM confidence calibration . they propose to use human cognition to elicit known "facts" and ask model to "reflect" over them . |
| Outcome: | The proposed method lowers the expected calibration error by 23.5% on multi-purpose QA tasks. |
GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios. |
| Approach: | They propose to use class-wise hardness to measure class-specific properties of data in the semantic embedding space by modeling class geometry in the . semantic embeddining space. |
| Outcome: | The proposed method surpasses instance-level metrics by over 59 percent on Pearson‘s correlation on measuring class-wise hardness. |
Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive. |
| Approach: | They propose to query a masked language model with cloze style prompts to obtain supervision signals. |
| Outcome: | The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%. |
Towards Reference-free Text Simplification Evaluation with a BERT Siamese Network Architecture (2023.findings-acl)
Copied to clipboard
| Challenge: | Text simplification (TS) aims to modify sentences to make their content and structure easier to understand. |
| Approach: | They propose a neural-network-based TS metric that uses a human reference to evaluate simplification and meaning preservation. |
| Outcome: | The proposed metric correlates better with human judgments for simplicity and meaning preservation than existing metrics. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
Copied to clipboard
Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge (2020.acl-main)
Copied to clipboard
| Challenge: | a new task is developed to categorize essential commonsense knowledge for the Winograd Schema Challenge . a previous work focused on the classification of commonsensical knowledge by pre-trained models . |
| Approach: | They propose a task that categorizes essential commonsense knowledge for the Winograd Schema Challenge . they leverage the collected reasons to identify plausible reasons from wrong ones . |
| Outcome: | The proposed task aims to shed light on commonsense knowledge needed for better reasoning . it is based on the collected reasons and a new task called WinoWhy . |
On Measuring the Intrinsic Few-Shot Hardness of Datasets (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has shown that few-shot learning is successful for pre-trained models, but there is no concrete understanding of when and why it is successful. |
| Approach: | They propose a simple metric that estimates few-shot hardness for a given dataset . they propose metric which exploits feature-space invariances between training and test samples . |
| Outcome: | The proposed metric better accounts for few-shot hardness compared to existing notions and is 8-100x faster to compute. |
Probing Toxic Content in Large Pre-Trained Language Models (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies on pre-trained language models have shown that they carry harmful biases towards different social groups. |
| Approach: | They propose a method to probe English, French, and Arabic PTLMs and quantify the potentially harmful content they convey with respect to a set of templates. |
| Outcome: | The proposed method analyzes PTLMs to predict masked tokens at the end of sentences to assess their toxicity. |
Leveraging Topic Relatedness for Argument Persuasion (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies of argumentation focus on the effects of factors such as source, audience, and language style, but the impact of exploiting the relationships among controversial topics is under-explored. |
| Approach: | They propose to model topic relatedness among controversial topics using topic embedding features and topic semantics features extracted from the arguments. |
| Outcome: | The proposed method improves predicting persuasiveness and generalizes to rare topics in a few-shot setting. |
MixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show the importance of document retrieval in the scientific domain. |
| Approach: | They propose a zero-shot approach to measure query-document similarity using atomic components in queries and documents to combine them into a united score. |
| Outcome: | The proposed approach outperforms previous document retrieval methods by 24.7%, 9.8%, and 6.9% on nDCG@5 with unsupervised, supervised, and LLM-based retrievers. |
SPHERE: An Evaluation Card for Human-AI Systems (2025.findings-acl)
Copied to clipboard
Dora Zhao, Qianou Ma, Xinran Zhao, Chenglei Si, Chenyang Yang, Ryan Louie, Ehud Reiter, Diyi Yang, Tongshuang Wu
| Challenge: | Existing evaluation methods and standards for human-AI systems are unclear, especially for large language models. |
| Approach: | They propose an evaluation card SPHERE which provides a template for evaluation protocols . they outline current evaluation practices and areas for improvement . |
| Outcome: | The evaluation card provides a template for designing evaluation protocols . it outlines current evaluation practices and areas for improvement . |
On the Representation Geometry of LoRA Model Merging (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for low-rank Adaptation (LoRA) fine-tuning focus on globally shared structure . combining SVD with CUR improves performance of LoRA model merging . |
| Approach: | They propose a training-free method that combines SVD and CUR decomposition to improve LoRA merging performance. |
| Outcome: | The proposed procedure improves on vision and language benchmarks. |
PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing PCR systems are not reliable in real applications due to weak semantic meanings of pronouns. |
| Approach: | They propose a benchmark and toolbox that evaluates performance of PCR systems from different perspectives. |
| Outcome: | The proposed benchmark and toolbox evaluates the performance of PCR systems from different perspectives. |
Dense X Retrieval: What Retrieval Granularity Should We Use? (2024.emnlp-main)
Copied to clipboard
| Challenge: | a learned dense retrieval model is often overlooked when using a corpus for inference, resulting in a design choice of retrieval unit . granularity of retrievals is important for both retrieval and downstream tasks . |
| Approach: | They propose a retrieval unit for dense retrieval that uses propositions to index corpus . propositions are defined as atomic expressions within text, each encapsulating a distinct factoid . |
| Outcome: | The proposed retrieval unit outperforms passage-level units on retrieval and downstream tasks. |
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code. |
| Approach: | They propose a structure-aware method that breaks large AST nodes into smaller chunks . this method generates self-contained, semantically coherent units across programming languages . |
| Outcome: | The proposed method boosts Recall@5 by 4.3 points on RepoEval retrieval and Pass@1 by 2.67 points on SWE-bench generation. |