Papers with foundation model
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)
Copied to clipboard
Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu
| Challenge: | Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks. |
| Approach: | They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations. |
| Outcome: | The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI. |
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2024.naacl-demo)
Copied to clipboard
| Challenge: | Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. |
| Approach: | They propose a toolkit to simplify the finetuning of general foundation models. |
| Outcome: | The proposed toolkit simplifies the domain- and task-aware finetuning of general foundation models with limited computing resources. |
Model Merging for Knowledge Editing (2025.acl-industry)
Copied to clipboard
Zichuan Fu, Xian Wu, Guojing Li, Yingying Zhang, Yefeng Zheng, Tianshi Ming, Yejing Wang, Wanyu Wang, Xiangyu Zhao
| Challenge: | Existing knowledge editing approaches struggle with sequential editing scenarios and harm the general capabilities of the model. |
| Approach: | They propose a framework that combines robust supervised fine-tuning and model merging for knowledge editing to combine supervised and supervised learning. |
| Outcome: | The proposed approach outperforms existing methods in sequential editing while preserving the original performance of the model. |
Language is All a Graph Needs (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing work on integrating graph problems into generative language modeling framework remains limited. |
| Approach: | They propose an LLM with instructions based on natural language to perform graph tasks. |
| Outcome: | The proposed model surpasses all GNN baselines on ogbn-arxiv, Cora and PubMed datasets and sheds light on generative LLMs as new foundation model for graph machine learning. |
Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025.acl-long)
Copied to clipboard
Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou
| Challenge: | Real-world RAG applications often encounter long-context input scenarios where redundant information and noise results in higher inference costs and reduced performance. |
| Approach: | They propose an efficient plug-and-play refiner that leverages the structural characteristics of long documents. |
| Outcome: | Experiments on seven QA datasets show that LongRefiner achieves competitive performance in various scenarios while using 10x fewer computational costs and latency compared to baseline. |
Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection (2024.findings-acl)
Copied to clipboard
| Challenge: | Gender bias has been widely observed in NLP models, which can perpetuate harmful stereotypes and discrimination. |
| Approach: | They construct a dataset to measure gender bias in stance detection using 36k samples . they find that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor . |
| Outcome: | The proposed dataset shows that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor. |
Unsupervised Human Preference Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for in-context learning and parameter-efficient fine-tuning fail to capture the complexity of human preferences, especially given the small, personal datasets individuals possess. |
| Approach: | They propose a method that uses small parameter models as preference agents to generate natural language rules that guide a larger, pre-trained model, enabling efficient personalization. |
| Outcome: | The proposed method outperforms baseline personalization methods on email and article datasets and significantly outperformed existing methods. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
Copied to clipboard
Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
| Approach: | They propose a retrieval-augmented generation framework that uses temporal logical rule-based retrieval and parameter-efficient instruction tuning to solve temporal knowledge forecasting challenges. |
| Outcome: | The proposed framework outperforms conventional methods in the temporal knowledge graph domain with low computation resources. |
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains (2024.findings-acl)
Copied to clipboard
Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. |
| Approach: | They propose an open-source LLM tailored for the biomedical domain that utilizes Mistral as its foundation model and pre-trained on PubMed Central. |
| Outcome: | The proposed model outperforms existing models on a benchmark comprising 10 established medical question-answering tasks in English and is competitive with proprietary models. |
WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for detecting LLM-generated texts falter when faced with adversarial perturbations, cross-domain shifts, and the rapid temporal evolution of the foundation model. |
| Approach: | They propose a framework that reformulates text detection as a signal processing task within the time-frequency domain. |
| Outcome: | The proposed framework achieves superior accuracy and robustness against sophisticated attacks and generalization across out-of-distribution topics. |
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking (2024.findings-acl)
Copied to clipboard
| Challenge: | Chinese Spell Checking (CSC) is a widely used technology for speech to text and optical character recognition. |
| Approach: | They propose to use Chinese rich semantic information to introduce large language models as the foundation model. |
| Outcome: | The proposed framework performs better on few-shot CSC task than existing methods. |
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning large language models are inefficient and redundant . a light-PEFT framework can be used to prune redundant parameters during training . |
| Approach: | They propose a parameter-efficient fine-tuning framework that freezes most parameters of the foundation model and finetuns only a small number of parameters. |
| Outcome: | The proposed framework achieves training and inference speedup, reduces memory usage, and maintains comparable performance and plug-and-play feature of PEFT. |
RecGPT: A Foundation Model for Sequential Recommendation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches fail in cold-start and cross-domain scenarios where new users or items lack sufficient interaction history. |
| Approach: | They propose a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities by deriving item representations exclusively from textual features. |
| Outcome: | The proposed model achieves zero-shot generalization capabilities in cold-start and cross-domain scenarios. |
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for metaphor interpretation are slow due to lack of annotated datasets and effective pre-trained language models. |
| Approach: | They propose a large annotated dataset and a PLM for the metaphor interpretation task. |
| Outcome: | The proposed method improves on metaphor identification and interpretation with comparable baselines on the new dataset. |
Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation (2023.acl-long)
Copied to clipboard
Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma
| Challenge: | Existing methods for learning representations of structured knowledge are limited to the minority of people with technical skills. |
| Approach: | They propose a large pretraining dataset and strategy for learning representations of text, tables, and SQL code that leverages the entire context of the problem. |
| Outcome: | The proposed model improves on two SQL tasks and shows a 1.7 and 2.2 percentage point improvement over existing methods. |
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Visual storytelling is a task of generating a story for a sequence of several temporally-ordered images or video frames. |
| Approach: | They propose a method that measures story quality in terms of human likeness regarding three key aspects highlighted in previous work: visual grounding, coherence, and repetitiveness. |
| Outcome: | The proposed method improves on the foundation model LLaVA but only slightly compared to TAPM, a 50-times smaller visual storytelling model. |
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models (2024.findings-emnlp)
Copied to clipboard
Jaeyoung Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel
| Challenge: | a growing influx of misinformation across news and social media is hampered by outdated foundation model training data. |
| Approach: | They propose to use large language models to scale up online policing mechanisms . they evaluate foundation model performance without continual updating . |
| Outcome: | The proposed model can improve performance without continual updating . the proposed model improves on two widely used benchmarks . |
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2025.findings-acl)
Copied to clipboard
| Challenge: | Visual Language Action models have shown promise in decision-making tasks, but have been neglected in previous work . |
| Approach: | They propose a new paradigm for visual language action models that enhances the foundation model prior to action-specific tuning by first post-training it on a curated set of visual and linguistic tasks using self-supervised learning. |
| Outcome: | The proposed model outperforms the best agent baseline on a diverse set of atomic tasks and surpasses imitation learning-based policies in Minecraft. |
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers . fine-timing on resource-constrained edge devices presents significant memory and computational demands . |
| Approach: | They propose a resource-efficient fine-tuning framework for LLMs specifically designed for edge devices. |
| Outcome: | Experiments show that MobiZO achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy. |
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)
Copied to clipboard
Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang
| Challenge: | Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks. |
| Approach: | They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration. |
| Outcome: | The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks. |
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies have shown that multi-task instruction tuning after pre-training greatly improves the model’s robustness and transfer ability, which is crucial for building a high-quality dialog system. |
| Approach: | They propose to use Task-aware Automatic Prompt generation (TAP) to automatically generate high-quality prompts from 15 dialog-related tasks. |
| Outcome: | The proposed model is robust to input prompts and capable of various dialog-related tasks. |
TVWorld: Foundations for Remote-Control TV Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing work on large vision–language models focuses on point-and-click interaction, while remote-control interaction is underexplored. |
| Approach: | They propose a topology-aware training framework that injects topology awareness into LVLMs. |
| Outcome: | The proposed model achieves 68.3% success rate on TVWorld-N, surpassing closed-source benchmarks and state-of-the-art (SOTA) benchmarks show that existing agents lack topology awareness for focus-based, long-horizon TV navigation. |