42 citations · 55 across the 15 of their papers we have counts for
18 papers
ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning
Binglin Zhou, Peng Shi, Ryo Kamoi +2
Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and tex…
Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
Jihyun Janice Ahn, Ryo Kamoi, Berk Atil +34
LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify…
Evaluating LLM-Simulated Conversations in Modeling Inconsistent and Uncollaborative Behaviors in Human Social Interaction
Ryo Kamoi, Ameya Godbole, Binglin Zhou +5
Simulating human conversations using large language models (LLMs) has emerged as a scalable methodology for modeling human social interaction. This paper reconsiders the evaluation…
One Model, All Roles: Multi-Turn, Multi-Agent Self-Play Reinforcement Learning for Conversational Social Intelligence
Bowen Jiang, Taiwei Shi, Ryo Kamoi +5
This paper introduces OMAR: One Model, All Roles, a reinforcement learning framework that enables AI to develop social intelligence through multi-turn, multi-agent conversational s…
Efficient PRM Training Data Synthesis via Formal Verification
Ryo Kamoi, Yusen Zhang, Nan Zhang +4
Process Reward Models (PRMs) have emerged as a promising approach for improving LLM reasoning capabilities by providing process supervision over reasoning traces. However, existing…
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
Yusen Zhang, Wenliang Zheng, Aashrith Madasu +14
High-resolution image (HRI) understanding aims to process images with a large number of pixels, such as pathological images and agricultural aerial images, both of which can exceed…
GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang +3
The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range…
VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information
Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das +2
Large Vision Language Models (LVLMs) have achieved remarkable performance in various vision-language tasks. However, it is still unclear how accurately LVLMs can perceive visual in…
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang +15
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, a…
Direct-Inverse Prompting: Analyzing LLMs' Discriminative Capacity in Self-Improving Generation
Jihyun Janice Ahn, Ryo Kamoi, Lu Cheng +2
Mainstream LLM research has primarily focused on enhancing their generative capabilities. However, even the most advanced LLMs experience uncertainty in their outputs, often produc…
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Ryo Kamoi, Yusen Zhang, Nan Zhang +2
Self-correction is an approach to improving responses from large language models (LLMs) by refining the responses using LLMs during inference. Prior work has proposed various self-…
Evaluating LLMs at Detecting Errors in LLM Responses
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou +12
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted o…
DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
Yilun Zhao, Yitao Long, Hongjun Liu +7
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-wo…
Fair Abstractive Summarization of Diverse Perspectives
Yusen Zhang, Nan Zhang, Yixin Liu +9
People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and pol…
WiCE: Real-World Entailment for Claims in Wikipedia
Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez +1
Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation. However, these represen…
Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
Ryo Kamoi, Tanya Goyal, Greg Durrett
Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most p…
Why is the Mahalanobis Distance Effective for Anomaly Detection?
Ryo Kamoi, Kei Kobayashi
The Mahalanobis distance-based confidence score, a recently proposed anomaly detection method for pre-trained neural classifiers, achieves state-of-the-art performance on both out-…
Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models is Sensitive to Prior Distribution Choice
Ryo Kamoi, Kei Kobayashi
Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is…