7 citations · 10 across the 11 of their papers we have counts for
11 papers
CoRA: Confidence-Rationale Alignment for Reliable Chain-of-Thought Reasoning
Juming Xiong, Weixin Liu, Kevin Guo +9
Chain-of-thought (CoT) reasoning can improve LLM performance, but high answer confidence may be misleading when the accompanying CoT rationale is plausible yet incomplete or poorly…
When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories
Avinash Baidya, Xinran Liang, Ruocheng Guo +2
Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail. This is challenging because supervision is ty…
It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty
Kevin H. Guo, Chao Yan, Avinash Baidya +5
Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behavior to sycophancy learned duri…
CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
Kevin H. Guo, Chao Yan, Avinash Baidya +5
Medical large language model (LLM) evaluations rely on simplified, exam-style benchmarks that rarely reflect the ambiguity of real-world medical inquiries. We introduce the CLinica…
Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Reliability
Kevin H. Guo, Chao Yan, Avinash Baidya +5
Large language models (LLMs) excel on static benchmarks, but their performance across multi-turn conversations, which better reflect real-world usage, remains understudied. Address…
Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning
Juming Xiong, Kevin Guo, Congning Ni +7
Large language models (LLMs) can achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet they often generate unnecessarily long reasoning paths that incu…
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning
Xiang Gao, Yuguang Yao, Qi Zhang +5
Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows.…
The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Avinash Baidya, Kamalika Das, Xiang Gao
Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot…
The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization
Dongsung Huh, Avinash Baidya
Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training.…
Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs
Avinash Baidya, Joel Dapello, James J. DiCarlo +1
While some convolutional neural networks (CNNs) have surpassed human visual abilities in object classification, they often struggle to recognize objects in images corrupted with di…
Renormalization in Open Quantum Field theory I: Scalar field theory
Avinash, Chandan Jana, R. Loganayagam +1
While the notion of open quantum systems is itself old, most of the existing studies deal with quantum mechanical systems rather than quantum field theories. After a brief review o…