Search Results for author:
Found 33 papers, 19 papers with code
Game-theoretic LLM: Agent Workflow for Negotiation Games
This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory.
SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement
SWE-Search extends traditional MCTS by incorporating a hybrid value function that leverages LLMs for both numerical value estimation and qualitative evaluation.
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic Systems
There is a pressing need for a unifying theory to illuminate the commonalities and differences in approaches and enable further progress.
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate
Large Language Models (LLMs) have shown exceptional results on current benchmarks when working individually.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
C-NERF: Representing Scene Changes as Directional Consistency Difference-based NeRF
Our approach surpasses state-of-the-art 2D change detection and NeRF-based methods by a significant margin.
Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis.
Don't Fine-Tune, Decode: Syntax Error-Free Tool Use via Constrained Decoding
Instruction-tuned large language models (LLMs) excel at many tasks but often fail to use external tools due to complicated and unfamiliar syntax constraints.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
Learning Concise and Descriptive Attributes for Visual Recognition
Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attributes that describe each class, then apply vision-language models to classify images via these attributes.
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables.
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their knowledge and uncertainty over questions.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark.
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
Oversmoothing is a central challenge of building more powerful Graph Neural Networks (GNNs).
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks
Training generalist agents is difficult across several axes, requiring us to deal with high-dimensional inputs (space), long horizons (time), and generalization to novel tasks.
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.
Emotion Recognition in Conversation using Probabilistic Soft Logic
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
NeuPSL: Neural Probabilistic Soft Logic
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Open Question Answering over Tables and Text
In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question.
Ranked #1 on
Question Answering
on OTT-QA
The gem5 Simulator: Version 20.0+
The open-source and community-supported gem5 simulator is one of the most popular tools for computer architecture research.
Hardware Architecture
GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
Motivated by optimal transport theory, we design the zero-sum game in GAT-GMM using a random linear generator and a softmax-based quadratic discriminator architecture, which leads to a non-convex concave minimax optimization problem.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
3) a hybrid model that combines heterogeneous information to find the answer.
Ranked #4 on
Question Answering
on HybridQA
Meta Module Network for Compositional Visual Reasoning
To design a more powerful NMN architecture for practical use, we propose Meta Module Network (MMN) centered on a novel meta module, which can take in function recipes and morph into diverse instance modules dynamically.
Fine-tune Bert for DocRED with Two-step Process
Modelling relations between multiple entities has attracted increasing attention recently, and a new dataset called DocRED has been collected in order to accelerate the research on the document-level relation extraction.
Ranked #55 on
Relation Extraction
on DocRED
Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection
Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs.
Scene Graph Generation via Conditional Random Fields
Scene graph, a graph representation of images that captures object instances and their relationships, offers a comprehensive understanding of an image.
A Variational Dirichlet Framework for Out-of-Distribution Detection
With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications.
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings
First, we introduce methods that learn network representations of entities in the knowledge graph capturing these varied aspects of similarity.
Variational Knowledge Graph Reasoning
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community.
Safer Classification by Synthesis
We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models.
