Papers with scoring
Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services (2025.naacl-industry)
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| Challenge: | Despite the practical importance of exam assembly, few methods exist to support educators during manual item retrieval for exam assembly tasks. |
| Approach: | They propose a mixed-integer programming re-ranking approach to improve relevance while mitigating bias on an industry-grade exam assembly platform. |
| Outcome: | The proposed approach improves relevance and reduces bias by 17% when compared to other methods on a real-world exam assembly platform. |
DART: An AIGT Detector using AMR of Rephrased Text (2025.naacl-short)
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| Challenge: | Existing methods for detecting AIGTs focus on probabilistic features, causing problems . performance of black-box detectors is low, and it is difficult to detect black- box models . |
| Approach: | They propose a detector that can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
| Outcome: | The proposed method can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
SCV: Light and Effective Multi-Vector Retrieval with Sequence Compressive Vectors (2025.coling-industry)
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| Challenge: | Recent advances in language models (LMs) have driven progress in information retrieval (IR), effectively extracting semantically relevant information. |
| Approach: | They propose a multi-vector retrieval framework with sequence compression vectors and a coarse-to-fine vector search framework that uses span compressive vectors to score documents. |
| Outcome: | The proposed approach achieves the fastest latency compared to other state-of-the-art models and can obtain competitive performance on both in-domain and out-of domain benchmark datasets. |
An Evaluation of Classifiers for Mapping Generative LLM Responses to Answer Options of Multiple-choice Questionnaires (2026.eacl-srw)
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| Challenge: | Large language models are used to generate multiple-choice style questionnaires that were originally intended for humans in persona simulations. |
| Approach: | They investigate the performance of smaller LLMs for mapping LLM outputs into the available answer options of multiple-choice questionnaires. |
| Outcome: | The proposed model underperforms on three datasets with differing answer option complexity. |
Deriving Language Models from Masked Language Models (2023.acl-short)
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| Challenge: | Masked language models do not define an explicit distribution over language, but they assume that tokens masked out are conditionally independent given the unmasked tokens. |
| Approach: | They propose to use a set of MLM's unary conditionals to construct a fully-connected Markov random field over the input to deduce an explicit joint distribution from MLMs. |
| Outcome: | The proposed method outperforms existing Markov random field-based approaches and outperformed the original model's conditionals. |
SMATCH++: Standardized and Extended Evaluation of Semantic Graphs (2023.findings-eacl)
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| Challenge: | Existing graph-alignment metrics that measure graph distances are not reliable, we show . metric is spread out and does not provide upper bounds for extended tasks. |
| Approach: | They propose a metric to measure a distance between graphs by aligning nodes and counting matching graph triples. |
| Outcome: | The proposed method reduces search space and improves scoring by reducing the number of errors. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List (2021.naacl-main)
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| Challenge: | Recent neural IR models shift towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. |
| Approach: | They propose a contextualized exact match retrieval architecture where scoring is based on overlapping query document tokens’ contextualized representations. |
| Outcome: | The proposed architecture outperforms classical lexical retrieval systems and state-of-the-art deep language models with smaller latency. |
Attention Is All You Need for Chinese Word Segmentation (2020.emnlp-main)
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| Challenge: | Recent work on Chinese word segmentation has been concerned about the following three perspectives. |
| Approach: | They propose to use a greedy decoding algorithm to improve Chinese word segmentation model. |
| Outcome: | The proposed model achieves state-of-the-art or comparable performance against strong baselines in strict closed test setting. |
SCRIPT: Self-Critic PreTraining of Transformers (2021.naacl-main)
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| Challenge: | Existing methods for representation learning of text are masked language modeling (MLM) a language model is trained to learn universal contextual embeddings, which are fine-tuned on a down-stream task. |
| Approach: | They propose a self-critic pretraining transformer for representation learning of text . they demonstrate improved sample-efficiency and improved performance over strong baselines . |
| Outcome: | The proposed model improves sample-efficiency and performance over strong baselines. |
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)
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| Challenge: | X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Approach: | They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Outcome: | The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality. |
“So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy (2021.emnlp-main)
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| Challenge: | Existing datasets for humour classification are limited due to the subjectivity of the content and the multiple interpretations of the data. |
| Approach: | They propose to annotate a multi-modal humour-annotated dataset using stand-up comedy clips and compute a humor quotient using the audience's laughter. |
| Outcome: | The proposed scoring mechanism is validated by comparing with manual scoring methods and achieves an accuracy of 0.813 in terms of QWK. |
TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation (2025.findings-emnlp)
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| Challenge: | Existing tool-learning methods often overlook fine-grained optimization of internal tool call details. |
| Approach: | They propose a training paradigm for constructing token-level tool-use preference datasets . reversed dataset construction is a method for creating high-quality, multi-turn tool-user datasets by reversing the generation flow. |
| Outcome: | a new training paradigm improves tool-using performance and generalizes results. |
Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning (2026.findings-acl)
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Haiyang Yu, Yuchuan Wu, Fan Shi, Jinghui Lu, Ke Niu, Xiaodong Ge, Minghan Zhuo, Jingqun Tang, Bin Li
| Challenge: | Existing document benchmarks focus on English printed texts or simplified Chinese . current vision-language models struggle with visual complexity and poor adaptability . |
| Approach: | They propose a benchmark to evaluate Chinese ancient documents' visual/linguistic complexity . ancient documents are valuable cultural heritage, but they face challenges in digitization and understanding . |
| Outcome: | the first benchmark for Chinese ancient documents evaluates VLMs from OCR to knowledge reasoning . ancient documents carry thousands of years of Chinese history and culture . traditional methods only scan images, while current models struggle with visual complexity . |