Search Results for author:
Found 35 papers, 30 papers with code
Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21. 0% across tasks and contexts.
Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only
In the absence of abundant reliable annotations for challenging tasks and contexts, how can we expand the frontier of LLM capabilities with potentially wrong answers?
Biased AI can Influence Political Decision-Making
As modern AI models become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged.
CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts
Taxonomies play a crucial role in various applications by providing a structural representation of knowledge.
Know Your Limits: A Survey of Abstention in Large Language Models
Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in LLM systems.
Can LLM Graph Reasoning Generalize beyond Pattern Memorization?
To this end, we propose the NLGift benchmark, an evaluation suite of LLM graph reasoning generalization: whether LLMs could go beyond semantic, numeric, structural, reasoning patterns in the synthetic training data and improve utility on real-world graph-based tasks.
Teaching LLMs to Abstain across Languages via Multilingual Feedback
Multilingual LLMs often have knowledge disparities across languages, with larger gaps in under-resourced languages.
Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration
Modular Pluralism is uniquely compatible with black-box LLMs and offers the modular control of adding new community LMs for previously underrepresented communities.
MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning
In this paper, we propose to change the static paradigm to an interactive one, develop systems that proactively ask questions to gather more information and respond reliably, and introduce an benchmark - MediQ - to evaluate question-asking ability in LLMs.
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Our experiments reveal that almost none of the existing detectors remain robust under all the attacks, and all detectors exhibit different loopholes.
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accuracy is paramount.
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering.
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection.
Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature of knowledge.
P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models
In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article.
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains.
Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models
The novel setting of geometric knowledge reasoning necessitates new LM abilities beyond existing atomic/linear multi-hop QA, such as backtracking, verifying facts and constraints, reasoning with uncertainty, and more.
Resolving Knowledge Conflicts in Large Language Models
To this end, we introduce an evaluation framework for simulating contextual knowledge conflicts and quantitatively evaluating to what extent LLMs achieve these goals.
Can Language Models Solve Graph Problems in Natural Language?
We then propose Build-a-Graph Prompting and Algorithmic Prompting, two instruction-based approaches to enhance LLMs in solving natural language graph problems.
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
Ultimately, Knowledge Card framework enables dynamic synthesis and updates of knowledge from diverse domains.
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks.
FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge
We propose FactKB, a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations.
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms.
Datavoidant: An AI System for Addressing Political Data Voids on Social Media
Independent journalists who combat disinformation in underrepresented communities have reported feeling overwhelmed because they lack the tools necessary to make sense of the information they monitor and address the data voids.
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction.
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
With the advent of pretrained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks.
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Further experiments bear out the quality of node representations learned with GraTO and the effectiveness of model architecture.
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles.
Tackling the Local Bias in Federated Graph Learning
To solve this problem, we propose a novel FGL framework to make the local models similar to the model trained in a centralized setting.
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge.
Legislator Representation Learning with Social Context and Expert Knowledge
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
