Search Results for author:
Found 17 papers, 5 papers with code
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning.
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance
Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc.
Large Language Models for Recommendation with Deliberative User Preference Alignment
We then introduce the Deliberative User Preference Alignment framework, designed to enhance reasoning capabilities by utilizing verbalized user feedback in a step-wise manner to tackle this task.
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation.
Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents
In the first stage, agents analyze their respective schema and communicate with each other to collect the schema information relevant to the question.
Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector
With the new RADAR dataset, we further develop a novel and effective iN-time Embedding-based AdveRSarial Image DEtection (NEARSIDE) method, which exploits a single vector that distilled from the hidden states of VLMs, which we call the attacking direction, to achieve the detection of adversarial images against benign ones in the input.
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated.
Large Language Models Empowered Personalized Web Agents
To overcome the limitation, we first formulate the task of LLM-empowered personalized Web agents, which integrate personalized data and user instructions to personalize instruction comprehension and action execution.
VideoQA in the Era of LLMs: An Empirical Study
Video Large Language Models (Video-LLMs) are flourishing and has advanced many video-language tasks.
CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG
To address this issue, we explore the task of "credibility-aware RAG", in which LLMs automatically adjust the influence of retrieved documents based on their credibility scores to counteract misinformation.
Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection
Self-detection for Large Language Models (LLMs) seeks to evaluate the trustworthiness of the LLM's output by leveraging its own capabilities, thereby alleviating the issue of output hallucination.
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e. g. SEC filings), where discrete reasoning capabilities are often required.
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs
Discrete reasoning over table-text documents (e. g., financial reports) gains increasing attention in recent two years.
Towards Complex Document Understanding By Discrete Reasoning
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural Language Processing and Computer Vision.
RDU: A Region-based Approach to Form-style Document Understanding
Key Information Extraction (KIE) is aimed at extracting structured information (e. g. key-value pairs) from form-style documents (e. g. invoices), which makes an important step towards intelligent document understanding.
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
Ranked #1 on
Question Answering
on TAT-QA
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents.
Machine Reading Comprehension
Open-Domain Question Answering
