42 citations · 55 across the 15 of their papers we have counts for
6 papers · 1 filter
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…