13 citations · 21 across the 10 of their papers we have counts for
12 papers
Training Reasoning Models to Generate Diverse Scientific Ideas
Manya Wadhwa, Pranav Narayanan Venkit, Hiroaki Hayashi +3
One of the first steps of a research pipeline is generating ideas. Large Language Models (LLMs) are increasingly used to automate this step, but most of this work optimizes the qua…
GENIE: A Fine-Grained Measure for Novelty
Ramya Namuduri, Manya Wadhwa, Anshun Asher Zheng +2
Large Language Models have consistently demonstrated a lack of creativity and diversity across tasks. Prior work has focused on addressing whether models are capable of generating…
CREATE: Testing LLMs for Associative Creativity
Manya Wadhwa, Tiasa Singha Roy, Harvey Lederman +2
A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate…
SkillFactory: Self-Distillation For Learning Cognitive Behaviors
Zayne Sprague, Jack Lu, Manya Wadhwa +3
Reasoning models leveraging long chains of thought employ various cognitive skills, such as verification of their answers, backtracking, retrying by an alternate method, and more.…
ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Liyan Tang, Grace Kim, Xinyu Zhao +12
Chart understanding presents a unique challenge for large vision-language models (LVLMs), as it requires the integration of sophisticated textual and visual reasoning capabilities.…
EvalAgent: Discovering Implicit Evaluation Criteria from the Web
Manya Wadhwa, Zayne Sprague, Chaitanya Malaviya +3
Evaluation of language model outputs on structured writing tasks is typically conducted with a number of desirable criteria presented to human evaluators or large language models (…
Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluation
Tuhina Tripathi, Manya Wadhwa, Greg Durrett +1
Large Language Models (LLMs) are widely used as proxies for human labelers in both training (Reinforcement Learning from AI Feedback) and large-scale response evaluation (LLM-as-a-…
QUDsim: Quantifying Discourse Similarities in LLM-Generated Text
Ramya Namuduri, Yating Wu, Anshun Asher Zheng +3
As large language models become increasingly capable at various writing tasks, their weakness at generating unique and creative content becomes a major liability. Although LLMs hav…
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez +7
Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra ``thin…
Learning to Refine with Fine-Grained Natural Language Feedback
Manya Wadhwa, Xinyu Zhao, Junyi Jessy Li +1
Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate w…
Using Natural Language Explanations to Rescale Human Judgments
Manya Wadhwa, Jifan Chen, Junyi Jessy Li +1
The rise of large language models (LLMs) has brought a critical need for high-quality human-labeled data, particularly for processes like human feedback and evaluation. A common pr…
Group Affect Prediction Using Multimodal Distributions
Saqib Shamsi, Bhanu Pratap Singh Rawat, Manya Wadhwa
We describe our approach towards building an efficient predictive model to detect emotions for a group of people in an image. We have proposed that training a Convolutional Neural…