Papers with fine-tuned language model
GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models (2026.acl-industry)
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| Challenge: | a growing number of online grocery shoppers are using category-level recommendation systems . traditional item-level methods face scalability and accuracy challenges . |
| Approach: | a new language model is developed to encode cyclical purchasing patterns into model parameters . the model is scalable and more business-aligned than traditional item-level methods . |
| Outcome: | a new language model outperforms standard methods in a live production environment . the proposed model achieves a 7.5% relative improvement in cart-adds per impression . |
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation (2023.findings-acl)
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| Challenge: | Crowdsourced dialogue corpora are limited in scale and topic coverage due to the expensive cost of data curation. |
| Approach: | They construct an augmented dataset for the emotional support conversation task using large language models for dialogue augmentation. |
| Outcome: | The proposed approach outperforms baselines of dialogue augmentation and improves the model's generalization ability to open-domain topics. |
LM-Cocktail: Resilient Tuning of Language Models via Model Merging (2024.findings-acl)
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| Challenge: | Pre-trained language models are continually fine-tuned to better support downstream applications. however, this operation may result in significant performance degeneration on general perspectives. |
| Approach: | They propose a method which enables pre-trained language models to stay resilient in general perspectives. |
| Outcome: | The proposed model achieves strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain. |
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements (2020.coling-main)
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Andrey Savchenko, Anton Alekseev, Sejeong Kwon, Elena Tutubalina, Evgeny Myasnikov, Sergey Nikolenko
| Challenge: | a recent study shows that image-based symbols are insufficient for symbolism prediction in visual advertising . a new method is proposed to help understand image advertisements . |
| Approach: | They propose a multimodal image-based classifier and object detection classifier for symbols . they propose 'symbolic' annotation tasks to help users understand ads' |
| Outcome: | The proposed system establishes state-of-the-art in symbolism prediction. |
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
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| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
| Outcome: | The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods. |
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content (2021.eacl-main)
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| Challenge: | Existing work on claim detection is built on the basis of a 'segregation' of claims across different domains. |
| Approach: | They propose a generalized generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model. |
| Outcome: | The proposed model outperforms baselines on six claim datasets by an average of 3 claim-F1 points and 2 claim-f1 points on the general-domain experiments. |
Solving Hard Analogy Questions with Relation Embedding Chains (2023.emnlp-main)
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| Challenge: | Existing approaches to model how concepts are related are incomplete and noisy. |
| Approach: | They propose to model relations as paths but associate their edges with relation embeddings. |
| Outcome: | The proposed representations are useful for solving hard analogy questions. |
PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics (2025.naacl-long)
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| Challenge: | Recent efforts to improve the quality of machine-generated natural language content have been limited due to the large token usage required by complex evaluation prompts. |
| Approach: | They propose a prompt optimization approach that uses a smaller, fine-tuned language model to compress input data for evaluation prompt, thus reducing token usage and computational cost when using larger LLMs for downstream evaluation. |
| Outcome: | The proposed approach reduces token usage and costs by 2.37 compared with larger LLMs for downstream evaluation. |
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings (2024.lrec-main)
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| Challenge: | The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which is used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings. |
| Approach: | They propose to use a dataset of sentences manually annotated for sentiment to train a robust sentiment identifier for parliamentary proceedings. |
| Outcome: | The proposed model performs very well on languages not seen during fine-tuning and additional fine- tuning data from other languages significantly improves the target parliament’s results. |