Papers with discriminative tasks
Exploring Knowledge Filtering for Retrieval-Augmented Discriminative Tasks (2025.findings-acl)
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| Challenge: | Recent studies have focused on generative tasks, while its potential in discriminative tasks remains largely unexplored. |
| Approach: | They propose a framework that incorporates knowledge filtering and prediction fusion mechanisms to improve model performance. |
| Outcome: | The proposed framework improves model performance on discriminative tasks by filtering out harmful knowledge and integrating it into the input context. |
Text-centric Alignment for Bridging Test-time Unseen Modality (2025.findings-emnlp)
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| Challenge: | a text-centric alignment method is used to handle unseen modalities and dynamic modality combinations at test time. |
| Approach: | They propose a text-centric alignment method that unifies different input modalities into a single semantic text representation by leveraging in-context learning with Large Language Models and uni-modal foundation models. |
| Outcome: | The proposed method unifies input modalities into a single semantic representation . it significantly improves the ability to manage unseen, diverse, and unpredictable modality combinations . |
Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
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| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
MUSCLE: A Model Update Strategy for Compatible LLM Evolution (2024.findings-emnlp)
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Jessica Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel, Hadi Pouransari
| Challenge: | Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. |
| Approach: | They propose a method to minimize the extent of instance regression in model updates by training a compatibility adapter that can enhance task fine-tuned language models. |
| Outcome: | The proposed approach reduces regressions by up to 40% when updating LLMs to newer versions while maintaining overall performance gains. |
Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy (2023.emnlp-main)
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| Challenge: | Pretrained language models (LMs) are used to discriminate on multiple-choice tasks that place probability mass on vocabulary tokens that aren’t among the given answer choices. |
| Approach: | They propose a mathematical formalism for SFC which allows us to quantify and bound its impact for the first time. |
| Outcome: | The proposed method eliminates the impact of SFC in the majority of instances. |
Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)
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| Challenge: | a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks . |
| Approach: | They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively . |
| Outcome: | The proposed method outperforms existing models and achieves a 3.3% improvement on average. |
Generationary or “How We Went beyond Word Sense Inventories and Learned to Gloss” (2020.emnlp-main)
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| Challenge: | Existing approaches to Word Sense Disambiguation use discrete word senses . however, many language users have different understandings of words . |
| Approach: | They propose a unified computational lexical semantics model that can produce contextually appropriate definitions. |
| Outcome: | The proposed model outperforms existing models in lexical semantics and discriminative tasks. |
A Universal Discriminator for Zero-Shot Generalization (2023.acl-long)
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| Challenge: | Generative modeling has been the dominant approach for large-scale pretraining and zeroshot generalization. |
| Approach: | They propose a discriminator that predicts whether a text sample comes from the true data distribution and which option has the highest probability of coming from the real data distribution. |
| Outcome: | The proposed discriminative approach outperforms GANs on a number of NLP tasks by 16.0%, 7.8%, and 11.5% respectively. |
PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding (2026.findings-acl)
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| Challenge: | Existing video understanding benchmarks do not adequately capture the pedagogical logic embedded in instructional videos. |
| Approach: | They propose a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement. |
| Outcome: | The proposed model performs well on discriminative tasks but degrades on higher-order pedagogical diagnosis, relying on parametric memory rather than grounded visual perception. |
GreekBART: The First Pretrained Greek Sequence-to-Sequence Model (2024.lrec-main)
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Iakovos Evdaimon, Hadi Abdine, Christos Xypolopoulos, Stamatis Outsios, Michalis Vazirgiannis, Giorgos Stamou
| Challenge: | Transfer learning has revolutionized the fields of Computer Vision and Natural Language Processing. |
| Approach: | They introduce a new language model, GreekBART, that is based on a BART-base architecture. |
| Outcome: | The proposed model outperforms BERT, GPT and other transformer-based models on discriminative tasks. |
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model (2021.emnlp-main)
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| Challenge: | Inductive transfer learning has taken the entire NLU field by storm, with models such as BERT and BART setting new state-of-the-art on countless tasks. |
| Approach: | They introduce a large-scale pretrained seq2seq model for French that is very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. |
| Outcome: | The proposed model outperforms existing models on discriminative and generative tasks on a French summarization dataset. |
PIXAR: Auto-Regressive Language Modeling in Pixel Space (2024.findings-acl)
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| Challenge: | Recent work shows the possibility of building open-vocabulary large language models that operate on pixel representations. |
| Approach: | They propose a pixel-based autoregressive LLM that performs generative tasks . they propose an adversarial pretraining stage that improves the readability and accuracy of PIXAR by 8.1 on LAMBADA and 8.5 on bAbI. |
| Outcome: | The proposed model performs free-form generative tasks while keeping the number of parameters on par with previous encoder-decoder models. |
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression (2024.findings-emnlp)
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| Challenge: | Prior work on compression prioritizes preserving perplexity, which is analogous to training loss. |
| Approach: | They examine the impact of model compression along four dimensions: degeneration harm, representational harm, dialect bias, and language modeling and downstream task performance. |
| Outcome: | The proposed compression methods can lead to unexpected consequences, the authors show . quantization preserves bias while pruning degrades quickly. |