2 citations · 2 across the 8 of their papers we have counts for
8 papers
A Benchmark for Deep Information Synthesis
Debjit Paul, Daniel Murphy, Milan Gritta +14
Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis. However, current e…
Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck
Meiru Zhang, Zaiqiao Meng, Nigel Collier
Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which causes them to overlook information…
DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theorems
Meiru Zhang, Philipp Borchert, Milan Gritta +1
Automating the formalization of mathematical statements for theorem proving remains a major challenge for Large Language Models (LLMs). LLMs struggle to identify and utilize the pr…
Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs
Xintong Tang, Meiru Zhang, Shang Xiao +5
Large language models (LLMs) are often constrained by rigid reasoning processes, limiting their ability to generate creative and diverse responses. To address this, a novel framewo…
Attention Instruction: Amplifying Attention in the Middle via Prompting
Meiru Zhang, Zaiqiao Meng, Nigel Collier
The context window of large language models has been extended to 128k tokens or more. However, language models still suffer from position bias and have difficulty in accessing and…
BAND: Biomedical Alert News Dataset
Zihao Fu, Meiru Zhang, Zaiqiao Meng +3
Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surv…
COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
Meiru Zhang, Yixuan Su, Zaiqiao Meng +2
Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity an…
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
Ognjen Rudovic, Meiru Zhang, Bjorn Schuller +1
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity. To achieve meaningful human-computer and hum…