Papers with MD
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. |
| Approach: | They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER . |
| Outcome: | The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages. |
Contextualized End-to-End Neural Entity Linking (2020.aacl-main)
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| Challenge: | a proposed entity linking model that disjointly applies MD and ED from the same contextualized BERT embeddings is able to generalize better. |
| Approach: | They propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED) they propose to use task-specific heads on top of shared BERT contextualized embeddings to learn MD and ED. |
| Outcome: | The proposed model achieves state-of-the-art results across a standard EL dataset and under a setting where hand-crafted candidate sets are not available. |
Mixed Distillation Helps Smaller Language Models Reason Better (2024.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive multiple step-by-step reasoning capabilities in recent NLP reasoning tasks. |
| Approach: | They propose a mixed distillation framework that distills multiple step-by-step reasoning abilities into smaller language models (SLMs) they leverage LLMs to generate multiple step by step reasoning rationales by sampling automatically. |
| Outcome: | The proposed framework outperforms existing models on SVAMP, GSM8K and ASDIV, while a single model generated by MD exceeds the comprehensive performance of two individual CoT and PoT distilled models. |
Adversarial Multi-task Learning for End-to-end Metaphor Detection (2023.findings-acl)
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| Challenge: | Existing methods to learn basic sense discrimination (BSD) are limited in training data. |
| Approach: | They propose a multi-task learning framework to transfer MD knowledge to basic sense discrimination using word sense disambiguation. |
| Outcome: | The proposed framework can mitigate the data scarcity problem in metaphor detection. |
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models (2025.naacl-long)
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Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). |
| Approach: | They propose to use a well-established method for text generation to extract token embeddings from multiple layers of LLMs and compute MD scores for each token. |
| Outcome: | The proposed method improves on existing methods and provides accurate and computationally efficient uncertainty scores for sequence-level selective generation and claim-level fact-checking tasks. |
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags (C18-1)
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| Challenge: | Named entity recognition (NER) taggers require external morphological disambiguation tools to function which are hard to obtain or non-existent for many languages. |
| Approach: | They propose a model which jointly learns NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. |
| Outcome: | The proposed model can be trained independently of the morphological annotation schemes, and it performs competitively. |
A Pointer Network Architecture for Joint Morphological Segmentation and Tagging (2020.findings-emnlp)
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| Challenge: | Morphological Disambiguation (MD) is a task of decomposing tokens into morphemes . a simple pipeline is used to segment and tagging raw tokens . |
| Approach: | They propose a new pointer network model that combines symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling. |
| Outcome: | The proposed model outperforms all previous reported results on Hebrew and Turkish . it uses morphological knowledge and the learning capacity of neural end-to-end modeling . |
Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation (2025.findings-acl)
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| Challenge: | Existing methods to mitiga hallucinations rely on sampling multiple full-length generations, which introduces significant response latency and becomes ineffective when the model consistently produces hallucines. |
| Approach: | They propose a framework that dynamically monitors the generation process and selectively applies in-process interventions to revise hallucination-prone tokens. |
| Outcome: | The proposed framework outperforms self-consistency-based approaches in both effectiveness and efficiency, achieving higher factual accuracy while significantly reducing computational overhead. |
MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance (2025.findings-acl)
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| Challenge: | Multi-document reasoning is an area of increasing relevance given LLM capabilities in handling longer-context inputs, but few benchmarks exist to rigorously examine model behavior in this setting. |
| Approach: | They propose a new dataset for evaluating LLMs on the task of multi-document reasoning that uses condensed structured seed knowledge to modify it through LLM-assisted edits. |
| Outcome: | The proposed method generates document sets and QA examples on a multi-document reasoning task using a synthetic generation process. |
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following (2025.acl-long)
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| Challenge: | Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. |
| Approach: | They propose a framework that generates high-quality synthetic MD instruction data over sets of articles via targeted prompts. |
| Outcome: | MDCure generates high-quality synthetic MD instruction data over sets of articles . evaluations show it improves over pre-trained models by up to 75.1% . |