Vinay Samuel

2026

1. 

   arXiv

   Recovering Diversity Without Losing Alignment: A DPO Recipe for Post-Trained LLMs

   Vinay Samuel, Yapei Chang, and Mohit Iyyer

2. 

   ICLR

   Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration

   Yijia Shao, Vinay Samuel, Yucheng Jiang, John Yang, and Diyi Yang

2025

1. 

   Findings of EMNLP

   PersonaGym: Evaluating Persona Agents and LLMs

   Vinay Samuel, Henry Peng Zou, Yue Zhou, Shreyas Chaudhari, Ashwin Kalyan, Tanmay Rajpurohit, Ameet Deshpande, Karthik R Narasimhan, and Vishvak Murahari

   Persona agents, which are LLM agents conditioned to act according to an assigned persona, enable contextually rich and user-aligned interactions across domains like education and healthcare.However, evaluating how faithfully these agents adhere to their personas remains a significant challenge, particularly in free-form settings that demand consistency across diverse, persona-relevant environments.We introduce PersonaGym, the first dynamic evaluation framework for persona agents, and PersonaScore, a human-aligned automatic metric grounded in decision theory that enables comprehensive large-scale evaluation. Our evaluation of 10 leading LLMs across 200 personas and 10,000 questions reveals significant advancement opportunities.For example, GPT-4.1 had the exact same PersonaScore as LLaMA-3-8b despite being a more recent and advanced closed-source model. Importantly, increased model size and complexity do not necessarily enhance persona agent capabilities, underscoring the need for algorithmic and architectural innovation toward faithful, performant persona agents.

2. 

   EMNLP

   CIE: Controlling Language Model Text Generations Using Continuous Signals

   Vinay Samuel, Harshita Diddee, Yiming Zhang, and Daphne Ippolito

   Aligning language models (LMs) with user intent is becoming increasingly relevant to enhance user experience.This calls for designing methods that can allow users to control the properties of the language that LMs generate, for example, controlling the length of the generation or the complexity of the language that gets chosen.Most existing work attempts to integrate users’ control by conditioning LM generations on natural language prompts or discrete control signals, which are often brittle and hard to scale.In this work, we are interested in continuous control signals, ones that exist along a spectrum that can’t easily be captured in a natural language prompt or via existing techniques in conditional generation.Through a case study in controlling the precise response-length of generations, we demonstrate how an LM can be finetuned to expect a control vector that is interpolated between a “low” and a “high” token embedding.Our method more reliably exerts response-length control than in-context learning methods or fine-tuning methods that represent the control signal as a discrete signal.

3. 

   COLM

   NoveltyBench: Evaluating Creativity and Diversity in Language Models

   Yiming Zhang, Harshita Diddee, Susan Holm, Hanchen Liu, Xinyue Liu, Vinay Samuel, Barry Wang, and Daphne Ippolito

4. 

   COLING

   Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges

   Vinay Samuel, Yue Zhou, and Henry Peng Zou

   As large language models achieve increasingly impressive results, questions arise about whether such performance is from generalizability or mere data memorization. Thus, numerous data contamination detection methods have been proposed. However, these approaches are often validated with traditional benchmarks and early-stage LLMs, leaving uncertainty about their effectiveness when evaluating state-of-the-art LLMs on the contamination of more challenging benchmarks. To address this gap and provide a dual investigation of SOTA LLM contamination status and detection method robustness, we evaluate five contamination detection approaches with four state-of-the-art LLMs across eight challenging datasets often used in modern LLM evaluation. Our analysis reveals that (1) Current methods have non-trivial limitations in their assumptions and practical applications; (2) Notable difficulties exist in detecting contamination introduced during instruction fine-tuning with answer augmentation; and (3) Limited consistencies between SOTA contamination detection techniques. These findings highlight the complexity of contamination detection in advanced LLMs and the urgent need for further research on robust and generalizable contamination evaluation.

2024

1. 

   ACL

   Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges

   Vinay Samuel, Houda Aynaou, Arijit Chowdhury, Karthik Venkat Ramanan, and Aman Chadha

   Large Language Models (LLMs) have demonstrated impressive zero-shot performance on a wide range of NLP tasks, demonstrating the ability to reason and apply common sense. A relevant application is to use them for creating high-quality synthetic datasets for downstream tasks. In this work, we probe whether GPT-4 can be used to augment existing extractive reading comprehension datasets. Automating data annotation processes has the potential to save large amounts of time, money, and effort that goes into manually labeling datasets. In this paper, we evaluate the performance of GPT-4 as a replacement for human annotators for low-resource reading comprehension tasks, by comparing performance after fine-tuning, and the cost associated with annotation. This work serves to be the first analysis of LLMs as synthetic data augmenters for QA systems, highlighting the unique opportunities and challenges. Additionally, we release augmented versions of low-resource datasets, that will allow the research community to create further benchmarks for evaluation of generated datasets. Github available at https://github.com/vsamuel2003/qa-gpt4

2. 

   Findings of ACL

   ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction

   Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, and Cornelia Caragea

   Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publicly available, and lack an in-depth human inspection across diverse domains. To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction. ImplicitAVE, sourced from the MAVE dataset, is carefully curated and expanded to include implicit AVE and multimodality, resulting in a refined dataset of 68k training and 1.6k testing data across five domains. We also explore the application of multimodal large language models (MLLMs) to implicit AVE, establishing a comprehensive benchmark for MLLMs on the ImplicitAVE dataset. Six recent MLLMs with eleven variants are evaluated across diverse settings, revealing that implicit value extraction remains a challenging task for MLLMs. The contributions of this work include the development and release of ImplicitAVE, and the exploration and benchmarking of various MLLMs for implicit AVE, providing valuable insights and potential future research directions. Dataset and code are available at https://github.com/HenryPengZou/ImplicitAVE.