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Works119 from public data
- 2,488
- CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing1,013
A framework called CRITIC is introduced that allows LLMs, which are essentially"black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools.
- Learning semantic representations using convolutional neural networks for web search730
This paper presents a series of new latent semantic models based on a convolutional neural network to learn low-dimensional semantic vectors for search queries and Web documents that significantly outperforms other se-mantic models in retrieval performance.
- A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval728
A new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents is proposed.
- Generation-Augmented Retrieval for Open-Domain Question Answering334
It is shown that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy, and as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance.
- ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving331
This paper proposes ToRA a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical problems by seamlessly integrating natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of tools.
- StoryGAN: A Sequential Conditional GAN for Story Visualization312
A new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework is proposed, which outperformed state-of-the-art models in image quality, contextual consistency metrics, and human evaluation.
- ReasoNet309
A novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks, which makes use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers.
- MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning210
Memory-Augmented Recurrent Transformer (MART) uses a memory module to augment the transformer architecture to generate a highly summarized memory state from the video segments and the sentence history so as to help better prediction of the next sentence (w.r.t. coreference and repetition aspects).
- Stochastic Answer Networks for Machine Reading Comprehension203
This work proposes a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension that achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset, the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehensionDataset.
- 192
- Explanations from Large Language Models Make Small Reasoners Better181
This paper considers the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment due to their low cost.
- A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models178
This work studies prompt-based low-resource learning of VL tasks with a sequence-to-sequence transformer model with prefix language modeling and masked language modeling, and observes that models with noisy prompts learn as quickly as hand-crafted prompts given larger training data.
- Language-Based Image Editing with Recurrent Attentive Models143
A generic modeling framework for two subtasks of LBIE: language-based image segmentation and image colorization using recurrent attentive models to fuse image and language features is proposed.
- Controllable Natural Language Generation with Contrastive Prefixes130
A novel lightweight framework for controllable GPT2 generation, which utilizes a set of small attribute-specific vectors, called prefixes, to steer natural language generation, and shows that its methods can guide generation towards the desired attributes while keeping high linguistic quality.
- 123
- Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models103
Synthetic prompting is introduced, a method that leverages a few handcrafted examples to prompt the model to generate more examples by itself, and selects effective demonstrations to elicit better reasoning.
- Learning personal + social latent factor model for social recommendation100
This paper develops a joint personal and social latent factor (PSLF) model that combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation and shows a significant improvement in terms of prediction accuracy criteria over the existing approaches.
- A Hybrid Retrieval-Generation Neural Conversation Model94
This paper proposes a hybrid neural conversation model that combines the merits of both response retrieval and generation methods and shows that the proposed model outperforms both retrieval- based methods and generation-based methods under both automatic evaluation metrics and human evaluation.
- 87
- 76
- UnitedQA: A Hybrid Approach for Open Domain Question Answering67
It is demonstrated that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles.
- Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation63
PLUG is a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks and achieves comparable performance with state-of-the-art methods in the fully-supervised setting and significantly outperforms other approaches in zero-shot and few-shot settings.
- 59
- Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension58
Experiments on three MRC tasks demonstrate the effectiveness of the proposed recurrent chunking mechanisms: they can obtain segments that are more likely to contain complete answers and at the same time provide sufficient contexts around the ground truth answers for better predictions.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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