27 citations · 51 across the 21 of their papers we have counts for
21 papers
Vision-Language Model Confidence Is Not a Property of the Answer
Reza Khanmohammadi, Erfan Miahi, Ivan Brugere +4
Vision-language models are increasingly deployed behind a confidence gate: the system reads how confident the model is in its answer and defers when confidence is low. This makes t…
Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding
Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley +2
LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritative-looking answer is sometime…
WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction
Chengzhi Liu, Yuzhe Yang, Sophia Xiao Pu +14
Multimodal large language models are increasingly deployed as long-horizon agents, where memory must do more than recall: it must track an evolving world, revise what has gone stal…
Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
Large vision-language models (LVLMs) suffer from visual ungroundedness: they can produce a fluent, confident, and even correct response driven entirely by language priors, with the…
Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research
Mirazul Haque, Antony Papadimitriou, Samuel Mensah +6
We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research. The benchmark assesses three…
Distill and Align Decomposition for Enhanced Claim Verification
Jabez Magomere, Elena Kochkina, Samuel Mensah +6
Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We…
How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
The miscalibration of Large Reasoning Models (LRMs) undermines their reliability in high-stakes domains, necessitating methods to accurately estimate the confidence of their long-f…
A Variational Approach for Mitigating Entity Bias in Relation Extraction
Samuel Mensah, Elena Kochkina, Jabez Magomere +3
Mitigating entity bias is a critical challenge in Relation Extraction (RE), where models often rely excessively on entities, resulting in poor generalization. This paper presents a…
GenPlanX. Generation of Plans and Execution
Daniel Borrajo, Giuseppe Canonaco, Tomás de la Rosa +10
Classical AI Planning techniques generate sequences of actions for complex tasks. However, they lack the ability to understand planning tasks when provided using natural language.…
Conservative Bias in Large Language Models: Measuring Relation Predictions
Toyin Aguda, Erik Wilson, Allan Anzagira +2
Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, frequently defaulting to No_Relation label when an appropriate option is unavailable…
Calibrating LLM Confidence by Probing Perturbed Representation Stability
Reza Khanmohammadi, Erfan Miahi, Mehrsa Mardikoraem +5
Miscalibration in Large Language Models (LLMs) undermines their reliability, highlighting the need for accurate confidence estimation. We introduce CCPS (Calibrating LLM Confidence…
FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking
Jabez Magomere, Elena Kochkina, Samuel Mensah +2
We introduce FinNLI, a benchmark dataset for Financial Natural Language Inference (FinNLI) across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transc…
Grounding LLM Reasoning with Knowledge Graphs
Alfonso Amayuelas, Joy Sain, Simerjot Kaur +1
Large Language Models (LLMs) excel at generating natural language answers, yet their outputs often remain unverifiable and difficult to trace. Knowledge Graphs (KGs) offer a comple…
FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline
Kuldeep Singh, Simerjot Kaur, Charese Smiley
Financial decision-making hinges on the analysis of relevant information embedded in the enormous volume of documents in the financial domain. To address this challenge, we develop…
Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency
Toyin Aguda, Suchetha Siddagangappa, Elena Kochkina +4
Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them. While Large Language Models (LLMs) have demonstrated rema…
Identifying Multiple Personalities in Large Language Models with External Evaluation
Xiaoyang Song, Yuta Adachi, Jessie Feng +6
As Large Language Models (LLMs) are integrated with human daily applications rapidly, many societal and ethical concerns are raised regarding the behavior of LLMs. One of the ways…
The Influence of Biomedical Research on Future Business Funding: Analyzing Scientific Impact and Content in Industrial Investments
Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley +4
This paper investigates the relationship between scientific innovation in biomedical sciences and its impact on industrial activities, focusing on how the historical impact and con…
DocLLM: A layout-aware generative language model for multimodal document understanding
Dongsheng Wang, Natraj Raman, Mathieu Sibue +6
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalit…
REFinD: Relation Extraction Financial Dataset
Simerjot Kaur, Charese Smiley, Akshat Gupta +5
A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailme…
InProC: Industry and Product/Service Code Classification
Simerjot Kaur, Andrea Stefanucci, Sameena Shah
Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the compan…
Parameterized Explanations for Investor / Company Matching
Simerjot Kaur, Ivan Brugere, Andrea Stefanucci +3
Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can signific…