Search Results for author:
Found 38 papers, 8 papers with code
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models
To address these challenges, we introduce a novel framework, ProtTeX, which tokenizes the protein sequences, structures, and textual information into a unified discrete space.
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model
Goal-oriented de novo molecule design, namely generating molecules with specific property or substructure constraints, is a crucial yet challenging task in drug discovery.
Prot2Chat: Protein LLM with Early Fusion of Sequence and Structure
Proteins play a pivotal role in living organisms, yet understanding their functions presents significant challenges, including the limited flexibility of classification-based methods, the inability to effectively leverage spatial structural information, and the lack of systematic evaluation metrics for protein Q&A systems.
Interleaved-Modal Chain-of-Thought
Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer.
PIP-MM: Pre-Integrating Prompt Information into Visual Encoding via Existing MLLM Structures
However, because the process of image encoding isprompt-agnostic, the extracted visual features only provide a coarsedescription of the image, impossible to focus on the requirements of theprompt.
AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning
In-context learning (ICL) facilitates Large Language Models (LLMs) exhibiting emergent ability on downstream tasks without updating billions of parameters.
Guiding ChatGPT to Generate Salient Domain Summaries
Then, we require ChatGPT to generate $k$ candidate summaries for the inference document at a time under the guidance of the retrieved demonstration.
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
Two strategies are designed to perform this iterative process: Prompt Chaining and Stepwise Prompt.
SelfCP: Compressing Over-Limit Prompt via the Frozen Large Language Model Itself
This paper proposes a Self-Compressor (SelfCP), which employs the target LLM itself to compress over-limit prompts into dense vectors while keeping the allowed prompts unmodified.
Unifying Demonstration Selection and Compression for In-Context Learning
In this paper, we propose an ICL framework UniICL, which Unifies demonstration selection and compression, and final response generation via a single frozen LLM.
CoUDA: Coherence Evaluation via Unified Data Augmentation
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
Personalized Large Language Model Assistant with Evolving Conditional Memory
With the rapid development of large language models, AI assistants like ChatGPT have become increasingly integrated into people's works and lives but are limited in personalized services.
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora.
RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check
In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models.
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Ranked #3 on
Text based Person Retrieval
on RSTPReid
Text-based Person Search without Parallel Image-Text Data
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space.
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods.
Few-shot Query-Focused Summarization with Prefix-Merging
Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization.
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
Revising Image-Text Retrieval via Multi-Modal Entailment
While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.
KE-QI: A Knowledge Enhanced Article Quality Identification Dataset
With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media.
General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
However, there is a big gap between the real input scenario and automatic generated corpus.
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably.
Ranked #25 on
Text Summarization
on GigaWord
Faithful to the Original: Fact Aware Neural Abstractive Summarization
While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.
Ranked #24 on
Text Summarization
on GigaWord
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
Improving Multi-Document Summarization via Text Classification
Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents.
Joint Copying and Restricted Generation for Paraphrase
In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.
AttSum: Joint Learning of Focusing and Summarization with Neural Attention
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
Both informativeness and readability of the collected summaries are verified by manual judgment.
Multi-Document Summarization via Discriminative Summary Reranking
However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets.
