7 citations · 39 across the 30 of their papers we have counts for
13 papers · 1 filter
Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only
Jihan Yao, Wenxuan Ding, Shangbin Feng +2
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? We focus on t…
Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
Shangbin Feng, Zifeng Wang, Yike Wang +9
We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms start…
JPEG-LM: LLMs as Image Generators with Canonical Codec Representations
Xiaochuang Han, Marjan Ghazvininejad, Pang Wei Koh +1
Recent work in image and video generation has been adopting the autoregressive LLM architecture due to its generality and potentially easy integration into multi-modal systems. The…
Know Your Limits: A Survey of Abstention in Large Language Models
Bingbing Wen, Jihan Yao, Shangbin Feng +4
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 syst…
Can LLM Graph Reasoning Generalize beyond Pattern Memorization?
Yizhuo Zhang, Heng Wang, Shangbin Feng +4
Large language models (LLMs) demonstrate great potential for problems with implicit graphical structures, while recent works seek to enhance the graph reasoning capabilities of LLM…
Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration
Shangbin Feng, Taylor Sorensen, Yuhan Liu +4
While existing alignment paradigms have been integral in developing large language models (LLMs), LLMs often learn an averaged human preference and struggle to model diverse prefer…
Teaching LLMs to Abstain across Languages via Multilingual Feedback
Shangbin Feng, Weijia Shi, Yike Wang +6
Multilingual LLMs often have knowledge disparities across languages, with larger gaps in under-resourced languages. Teaching LLMs to abstain in the face of knowledge gaps is thus a…
MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning
Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng +4
Users typically engage with LLMs interactively, yet most existing benchmarks evaluate them in a static, single-turn format, posing reliability concerns in interactive scenarios. We…
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers
Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar +4
Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly en…
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Yichen Wang, Shangbin Feng, Abe Bohan Hou +5
The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse. The goal of our study is to stress tes…
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
Herun Wan, Shangbin Feng, Zhaoxuan Tan +3
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 accu…
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Shangbin Feng, Herun Wan, Ningnan Wang +3
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we b…
Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Shangbin Feng, Weijia Shi, Yike Wang +3
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…