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Found 20 papers, 9 papers with code
Let's Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models
While Chain of Thought (CoT) prompting approaches have significantly consolidated the reasoning capabilities of large language models (LLMs), they still face limitations that require extensive human effort or have performance needs to be improved.
Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
To investigate RPAs' performance when faced with different types of conflicting requests, we develop an evaluation benchmark that includes contextual knowledge conflicting requests, parametric knowledge conflicting requests, and non-conflicting requests to assess RPAs' ability to identify conflicts and refuse to answer appropriately without over-refusing.
CJEval: A Benchmark for Assessing Large Language Models Using Chinese Junior High School Exam Data
Online education platforms have significantly transformed the dissemination of educational resources by providing a dynamic and digital infrastructure.
VITA: Towards Open-Source Interactive Omni Multimodal LLM
The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas.
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
To address these issues, we propose a novel large language model specifically designed for the Chinese financial domain, named SNFinLLM.
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
Direct Preference Optimization (DPO) has emerged as a prominent algorithm for the direct and robust alignment of Large Language Models (LLMs) with human preferences, offering a more straightforward alternative to the complex Reinforcement Learning from Human Feedback (RLHF).
CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments
We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent's effectiveness in a real-world use case.
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema
Herein, some works have proposed Automatic Prompt Optimization (APO), to optimize naive prompts according to task outputs of given in-box testing models, with the help of advanced LLMs (e. g., GPT-4) in an ad-hoc way.
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks.
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Our framework comprises a core decomposer agent for Text-to-SQL generation with few-shot chain-of-thought reasoning, accompanied by two auxiliary agents that utilize external tools or models to acquire smaller sub-databases and refine erroneous SQL queries.
VKIE: The Application of Key Information Extraction on Video Text
Extracting structured information from videos is critical for numerous downstream applications in the industry.
Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation
Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently.
MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations.
OSAN: A One-Stage Alignment Network To Unify Multimodal Alignment and Unsupervised Domain Adaptation
An intuitive way to handle these two problems is to fulfill these tasks in two separate stages: aligning modalities followed by domain adaptation, or vice versa.
Grafting Pre-trained Models for Multimodal Headline Generation
In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model.
Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document
In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document.
Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning
Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale.
OS-MSL: One Stage Multimodal Sequential Link Framework for Scene Segmentation and Classification
In this paper, from an alternate perspective to overcome the above challenges, we unite these two tasks into one task by a new form of predicting shots link: a link connects two adjacent shots, indicating that they belong to the same scene or category.
RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction
To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance.
Ranked #1 on
Document-level Event Extraction
on ChFinAnn
Contrastive Graph Multimodal Model for Text Classification in Videos
However, to our knowledge, there is no existing work focused on the second step of video text classification, which will limit the guidance to downstream tasks such as video indexing and browsing.
